Week 14 NFL Time Margin Ratings and Predictions:

What is Time Margin?:

I have discussed my original Time Margin Metric in previous articles on the site. However I have updated some of the methodology for calculating Team’s Time Margin and leveraged it into a predictive model that can be used to make predictions for games and spreads.

The essence behind the rating is to find the amount of time the winning football team took to put the game away. In theory if a team put the game away with 7 minutes left in the third quarter, their Time Margin would be 22. This is due to the fact there were 22 minutes left in the game. It serves as an alternative to the usage of scoring margin to determine how dominant football teams are. Scoring Margin can often be inflated by garbage time touchdowns for the losing team, or a late TD by the winning team that makes a back and fourth game look like a double digit loss. But how exactly do I go about determining how long a team took to put a game to bed?

Here is an example that also illuminates the benefits of this metric:

The Dolphins’ Time Margin in their 30-15 win over the Texans a couple weeks ago would have been 48. Which is much more of a fair measure of how dominant the team was to anyone who watched the game. The logic behind the number 48 is that at 3 minutes left in the first the Dolphins went up by more than one possession (>/=8 Points). As the game went on the Texans failed to get the game within one score. The inputs into the metric are play by play scoring summaries. The logic behind taking the time left in the game where the winning team went up by more than 8 points and never let their lead go under that margin is as follows: hypothetically the trailing team can tie the game on any single play if the margin is 8 points or less. Even if the team is on defense they could get a Pick 6 and a two point conversion, and then the game is tied.

Now lets say the the Texans somehow came back from the 30-0 deficit and made the score 30-23 with 8 minutes left. The Time Margin would have been +5 for the Dolphins and -5 for the Texans if the score remained the same, as any game that ends in a one possession game (Scoring Margin >/= 8 Points) is assigned +5 for the winning team and -5 for the losing team. Additionally any game that has a team by going up more than one possession in the final 5 minutes is also assigned the same values even if the winning team scores 3 touchdowns in the final 5 minutes to make it a 22 point win. In my opinion the smaller margin is more indicative of how the game actually was played out, as if the game was competitive and close for 55 minutes a touchdown or two scored after the winning team puts the game away, does not actually change how dominant or not dominant they were for the entirety of the game.

Now lets. go over another example to fine tune the calculation and understanding of the metric, that also demonstrates the benefits. The Cowboys 54-19 win over the Colts last week.

As you can see this game was clearly competitive and in-question going into the fourth quarter as the Cowboys were up by just 2 points heading into the final fourth of the game. The Cowboys won the game by 35 points, now this contradicts what actually happened on the field as the Cowboys struggled to pull away into somewhat late in the game. The Cowboys’ Time Margin would have been +14 and the Colts -14. As Dak Prescott’s Passing TD to Michael Gallup with 13:52 left in the fourth put the Cowboys up by 9. Then the Cowboys strung up four more touchdowns to inflate the final score to look like a massive blowout.

This metric can be used for both a a Simple Rating System which is what discussed in this article or a more advanced Least Squares Rating System using Matrix Algebra similar to Kenneth Massey’s system for rating college football teams.

How the Ratings are calculated?

The ratings in this article are calculated as follows:

TM=Time Margin

G=Games Played

Opp= Opponents

Time Margin Rating=ΣTM/GP + ΣOpp’s Avg TM/GP

For example the Buffalo Bill’s average Time Margin of this year was 12.92. The average time margin of their opponents was .80 therefore their rating would be 13.71. However since we are adding a strength of schedule rating to each team’s raw time margin, each team’s rating also changes. As in the first iteration for Strength of Schedule is only inclusive of the raw margin. So we then go through the process again of averaging the opponent’s first SOS adjusted Time Margins to create a new strength of schedule rating. Then we add it to the team’s raw time margin. We do this for 3 iterations just to streamline calculation and avoid the tedious iterative process. As this metric requires intense and tedious data calculations. Eventually we will be going through way more than 3 iterations until the ratings don’t change which summates into the true rating. For example the average of the Bills’ opponents First SOS adjusted Time Margin’s was 2.08 then their second rating iteration was 14.99. Then their third SOS iteration was 2.29 resulting in their now final rating of 15.21.

This measure functions both as a predictive and retrodictive rating. The ratings can be regressed to get their equivalent in typical points which can be used to acquire betting lines and odds for individual games. Therefore it can be used to make predictions for games. However it also measures how dominant teams were are given their strength of schedule. The Bills rating. of15.21 means they performed 15.21 Time Margin points better than the average team would have given their strength of schedule.

The method in calculating betting lines and predictions based on team’s time margins is as follows:

You have the team’s standard margin of victory as the response variable and regress their raw time margins to it to set up an equation that predicts a teams scoring margin based on their time margin.

The formula is:

Predicted Scoring Margin= .005 + (Time Margin * .656)

In practice we take the difference between the two team’s time margin rating then plug that difference into hte above formula. That is the expected point margin between the team, then we add 2 points to the home team to adjust for commonly accepted home field advantage.

Not surprisingly there is a statistically strong relationship between Time Margin and Scoring Margin as the correlation is .91.

Week 14 Ratings:

  1. Buffalo Bills 9-3: 15.21
  2. Philadelphia Eagles 11-1: 13.08
  3. Kansas City Chiefs 9-3: 13.01
  4. Miami Dolphins 8-4: 12.12
  5. Cincinnati Bengals 8-4: 10.79
  6. Dallas Cowboys 9-3: 7.60
  7. Baltimore Ravens 8-4: 6.51
  8. San Francisco 49ers 8-4: 6.12
  9. Detroit Lions 5-7: 5.82
  10. Minnesota Vikings 10-2: 3.98
  11. New York Jets 7-5: 1.79
  12. New England Patriots 6-6: 1.78
  13. Washington Commanders 7-5-1: -0.13
  14. Cleveland Browns 5-7: -1.33
  15. New York Giants 7-4-1: -1.60
  16. Tennessee Titans 7-5: -2.22
  17. Pittsburgh Steelers 5-7: -2.49
  18. Green Bay Packers 5-8: -2.52
  19. Seattle Seahawks 7-5: -2.69
  20. Tampa Bay Buccaneers 6-6: -4.41
  21. New Orleans Saints 4-9: -4.66
  22. Jacksonville Jagaurs 4-8: -4.68
  23. Carolina Panthers 4-8: -4.94
  24. Chicago Bears 3-10: -4.99
  25. Atlanta Falcons 5-8: -5.07
  26. Los Angeles Chargers 6-6: -5.70
  27. Los Angeles Rams 4-8: -6.09
  28. Arizona Cardinals 4-8: -6.32
  29. Denver Broncos 3-9: -7.28
  30. Indianapolis Colts 4-8-1: -7.78
  31. Las Vegas Raiders 5-8: -8.07
  32. Houston Texans 1-10-1: -12.96

Week 14 Time Margin Betting Lines & Conf/Div Ratings:

Time Margin LineVegas Lines
LV @ LARLAR -3.5LV -6.5
NYJ @ BUFBUF -11BUF -9.5
CLE @ CINCIN -10CIN -6.0
HOU @ DALDAL -15.5DAL -16.5
MIN @ DETDET -3DET -2.5
JAX @ TENTEN -3.5TEN -4.0
PHI @ NYGPHI -7.5PHI -7.0
BAL @ PITBAL -4PIT -2.5
KC @ DENKC -11.5KC -9.5
TB @ SFSF -9SF -3.5
CAR @ SEASEA -3.5SEA -3.5
MIA @ LACMIA -9.5MIA -3.0
NE @ ARINE -3.5NE -1.5
MeanMedian
Conf RatingsAFC0.5438548645-1.775709544
NFC-0.4259349691-2.603939159
Div RatingsAFC East7.726217586
AFC North3.36917259
AFC South-6.910705549
AFC West-2.009265169
NFC East4.737859214
NFC North0.5721481719
NFC South-4.768610407
NFC West-2.245136855

Full Data and Strength of Schedule:

2020-2021 Final NFL Time Margin Ratings

Tampa Bay tight end Rob Gronkowski celebrates as he scores a touchdown during the Super Bowl LV game of Tampa Bay Buccaneers versus Kansas City Chiefs at Raymond James Stadium in Tampa, Fla., on Sunday, Feb. 7, 2021. (Stephen M. Dowell/Orlando Sentinel via AP)

Note about Site Content:

I’d like to apologize about the lack of content over the past year, It has been a crazy year for me as it has been for everyone. I hope everyone is staying safe, wearing a mask, socially distancing, and being healthy. I hope to publish more articles in 2021.

About this Article:

Some of the utility of this article is lost due to the fact the Buccaneers have just won the super bowl, but I have had this data on my computer, and wanted to publish it in the public domain. This article presents my Time Margin ratings which I have published before on this site.

About Time Margin Ratings:

My time margin ratings are meant to be an alternative method to look how dominant teams are, in contrast to the usual Margin of Victory metric. Often times a game’s margin can be misleading. You can have the winning team dominate for the majority of the game, and then they sub in their back-ups and in turn allowing the opposing team to score garbage time touchdowns that sets up a final score that is misleading of the actual game. You can also have a game that is back and forth and but scores a TD late in the game to make it a two possession game.

My metric tries to solve these issues by evaluating the point in the game in which the winning team put the game away for good. I consider the point in which the winning team went up by more than one possesion and then did not allow the game to get within one possesion, that point. In other words it is the point int he game when the losing team could not tie the game with any sort of one play. We will use this Packers vs. Giants game from 2019 as a case study in how it would work.

Screenshot 2019-12-05 18.46.41.png

The scoring margin would clearly be +18 for the Packers however the time margin would be +14 for the Packers because that is the point in which the Packers went from a 4 point lead to an 11 point lead, and the Giants failed to get the game back into a one possession game.

There are other adjustments as well in terms of the rating system. All one possesion games (Within 8 points) are assigned a margin of +5 for the winning team and -5 for the losing team. If a team goes up by two possession within the last 5 minutes of a game, the teams are also assigned a +5/-5. Additioanlly, there are adjustments for home & away games. Statistically speaking these tend to cancel out at the end of the season, since baring weird circumstances all teams play the same ammount of home games as away games. The home-away adjustment would in turn lead the Packers to have a final margin of +15.59. The adjustment is +1.59 for being away and -1.59 for being the home team. This was derived from regressing time margin on actual scoring margin, and then using the typical agreed upon 1.5-2 point advantage that being the home team offers. In past seasons the time metric used an adjustment of 3.18 however I decreased it since there were either no fans allowed or less fans allowed than usual due to COVID. Over the course of the regular season the adjustments are more helpful, as well as looking at singular team game performances. Additionally tie games are assigned a 0 margin.

Final Formulas:

The final Time-Margin Rating formula is as follows: (.5*Mean Time Margin+.5*Median Time Margin)+(2/3*Mean Opponent Time Margin+1/3*Median Opponent Time Margin)

Additionally I added a Win-Loss Adjusted Time Margin Rating, to make a more retrodictive rating rather than predictive. Retrodictive means that is evaluating more so how good the team has been overall the season, rather than trying to predict how they will play against others. Therefore I would advise readers to look at the non W-L adjusted time margin rating to make predictions in the future, and to look at the W-L adjusted one to evaluate the team’s overall season performance within the Time Margin context. The W-L adjusted rating allocates +7 points for each individual game won, -7 for each loss, and 0 for each tie. The W-L adjusted formula is simple and is as follows:

If the Team has W-L% greater or equal to .5=((.5*Mean Time Margin)+.(5*Median Time Margin))*(Win-Loss %))

If the Team has a W-L% less than .5=((.5*Mean Time Margin)+.(5*Median Time Margin))*(1-Win-Loss %))

The same SOS adjustments are applied in the same manner as the regular Time Margin formula.

2020 NFL Regular Season Time Margin Ratings:

  1. Baltimore Ravens 11-5: 15.1
  2. Buffalo Bills 13-3: 8.4
  3. Green Bay Packers 13-3: 7.7
  4. New Orleans Saints 12-4: 6.9
  5. Kansas City Chiefs 14-2: 6.1
  6. Tampa Bay Buccaneers 11-5: 5.9
  7. Miami Dolphins 10-6: 5.1
  8. Pittsburgh Steelers 12-4: 5.1
  9. Los Angeles Rams 10-6: 4.0
  10. Seattle Seahawks 12-4: 3.1
  11. Tennessee Titans 11-5: 2.7
  12. Indianapolis Colts 11-5: 1.4
  13. Arizona Cardinals 8-8: 0.5
  14. Cleveland Browns 11-5: -0.7
  15. Chicago Bears 8-8: -1.6
  16. New England Patriots 7-9: -1.7
  17. Las Vegas Raiders 8-8: -1.9
  18. Carolina Panthers 5-11: -2.2
  19. San Francisco 49ers 6-10: -2.5
  20. Atlanta Falcons 4-12: -2.8
  21. Philadelphia Eagles 4-11-1: -3.3
  22. Los Angeles Chargers 7-9: -3.9
  23. Dallas Cowboys 6-10: -4.9
  24. Denver Broncos 5-11: -5.1
  25. Minnesota Vikings 7-9: -5.8
  26. Washington Football Team 7-9: -6.0
  27. New York Giants 6-10: -6.3
  28. Detroit Lions 5-11: -6.4
  29. Houston Texans 4-12: -6.4
  30. Cincinnati Bengals 4-11-1: -7.8
  31. Jacksonville Jaguars 1-15: -9.5
  32. New York Jets 2-14: -14.2

2020 NFL Regular Season Time Margin W-L% Adjusted Ratings:

  1. Baltimore Ravens 11-5: 13.0
  2. Buffalo Bills 13-3: 11.4
  3. Kansas City Chiefs 14-2: 10.5
  4. Green Bay Packers 13-3: 10.4
  5. New Orleans Saints 12-4: 8.8
  6. Pittsburgh Steelers 12-4: 7.4
  7. Tampa Bay Buccaneers 11-5: 7.0
  8. Seattle Seahawks 12-4: 5.7
  9. Miami Dolphions 10-6: 4.9
  10. Los Angeles Rams 10-6: 4.9
  11. Tennessee Titans 11-5: 4.7
  12. Indianapolis Colts 11-5: 3.4
  13. Cleveland Browns 11-5: 2.3
  14. Arizona Cardinals 8-8: -0.7
  15. Las Vegas Raiders 8-8: -0.9
  16. Chicago Bears 8-8: -1.5
  17. New England Patriots 7-9: -2.4
  18. San Francisco 49ers 6-10: -3.5
  19. Carolina Panthers 5-11: -4.8
  20. Los Angeles Chargers 7-9: -5.1
  21. Minnesota Vikings 7-9: -5.4
  22. Philadelphia Eagles 4-11-1: -5.9
  23. Atlanta Falcons 4-12: -5.9
  24. Denver Broncos 5-11: -6.4
  25. Dallas Cowboys 6-10: -6.5
  26. Washington Football Team 7-9: -6.5
  27. New York Giants 6-10: -6.7
  28. Detroit Lions 5-11: -7.3
  29. Houston Texans 4-12: -8.4
  30. Cincinnati Bengals 4-11-1: -8.8
  31. Jacksonville Jaguars 1-15: -14.9
  32. New York Jets 2-14: -17.5

2020 NFL Regular Season Time Margin Aggregate (Average of W-L and Regular Metrics):

  1. Baltimore Ravens 11-5: 14.0
  2. Buffalo Bills 13-3: 9.9
  3. Green Bay Packers 13-3: 9.0
  4. Kansas City Chiefs 14-2: 8.3
  5. New Orleans Saints 12-4: 7.8
  6. Tampa Bay Buccaneers 11-5: 6.5
  7. Pittsburgh Steelers 12-4: 6.3
  8. Miami Dolphins 10-6: 5.0
  9. Los Angeles Rams 10-6: 4.4
  10. Seattle Seahawks 12-4: 4.4
  11. Tennessee Titans 11-5: 3.7
  12. Indianapolis Colts 11-5: 2.4
  13. Cleveland Browns 11-5: 0.8
  14. Arizona Cardinals 8-8: -0.1
  15. Las Vegas Raiders 8-8: -1.4
  16. Chicago Bears 8-8: -1.5
  17. New England Patriots 7-9: -2.0
  18. San Francisco 49ers 6-10: -3.0
  19. Carolina Panthers 5-11: -3.5
  20. Atlanta Falcons 4-12: -4.4
  21. Los Angeles Chargers 7-9: -4.5
  22. Philadelphia Eagles 4-11-1: -4.6
  23. Minnesota Vikings 7-9: -5.6
  24. Dallas Cowboys 6-10: -5.7
  25. Denver Broncos 5-11: -5.8
  26. Washington Football Team 7-9: -6.3
  27. New York Giants 6-10: -6.5
  28. Detroit Lions 5-11: -6.9
  29. Houston Texans 4-12: -7.4
  30. Cincinnati Bengals 4-11-1: -8.3
  31. Jacksonville Jaguars 1-15: -12.2
  32. New York Jets 2-14: -15.8

Strength of Schedule Ratings:

  1. New York Jets: 2.6
  2. San Francisco 49ers: 2.2
  3. New England Patriots: 1.8
  4. Jacksonville Jaguars: 1.7
  5. Cincinnati Bengals: 1.5
  6. Detroit Lions: 1.3
  7. Houston Texans: 1.3
  8. Denver Broncos: 1.3
  9. Atlanta Falcons: 0.5
  10. Philadelphia Eagles: 0.3
  11. Carolina Panthers: 0.2
  12. Minnesota Vikings: 0.1
  13. Las Vegas Raiders: 0.1
  14. New York Giants: -0.2
  15. Buffalo Bills -0.4
  16. Los Angeles Rams: -0.9
  17. Tampa Bay Buccaneers: -1.0
  18. Kansas City Chiefs: -1.1
  19. Chicago Bears: -1.3
  20. New Orleans Saints: -1.4
  21. Pittsburgh Steelers: -1.4
  22. Los Angeles Chargers: -1.5
  23. Tennessee Titans: -1.7
  24. Dallas Cowboys: -1.8
  25. Cleveland Browns: -1.8
  26. Arizona Cardinals: -1.9
  27. Seattle Seahawks: -2.1
  28. Washington Football Team: -2.2
  29. Baltimore Ravens: -2.2
  30. Miami Dolphins: -2.6
  31. Green Bay Packers: -2.7
  32. Indianapolis Colts: -2.8

Divisional Ratings:

  1. AFC North: 3.3
  2. NFC West: 1.8
  3. NFC South: 1.6
  4. AFC East: 0.4
  5. AFC West: -1.9
  6. NFC North: -2.4
  7. AFC South: -2.9
  8. NFC East: -5.9

Key Takeaways:

–The Baltimore Ravens dominated yet underperformed: The first thing that stands out from these ratings were the fact that the Baltimore Ravens ranked #1 in all three of the ratings: regular time mat, W-L Adj Time Mat, and Aggregate Time Mat. Despite this the team just finished 11-5 and suffered a second round exit in the playoffs. Although 11-5 is nothing to scoff over, it is not the record you’d expect for a team who was head and shoulders more dominant over the rest of the team’s in the league. The Ravens had 7 wins with a 30+ Time Margin. These wins came over the #13 Browns, #29 Texans, #26 Washington, #30 Bengals, #32 Jaguars, #27 Giants, and #30 Bengals. Clearly has Baltimore piled up their impressive margins against the bottom dwellers of the league however they did perform well against some good competition. As they had two wins over the #13 Browns and one win over #12 Colts. This adds cadence to the theme that statistics and metrics are only useful when used in context.

–Time Margin’s Not Surprising Undervaluing of the Browns: The Browns’ success this season was one of the biggest stories of the NFL year. Going 11-5, head Coach Kevin Stefanski won a very deserving coach of the year. In addition the team won their first playoff game in over 25 years. The team also picked up key wins against playoff contenders such as the Titans and Steelers. However the team had a negative Time Margin Rating of -0.7 despite winning 6 more games than they lost. This is not shocking because the team also had a negative -11 cumulative point differential on the year. With a closer look at their game logs the 9 of the 11 brown wins were either by one possession or scored the game closing score within the last 5 minutes of the game (Time Margin < 5). Also all of their wins besides one had a Time Margin in single digits. Lastly two of their losses against the Ravens and Steelers had a time margins of -33 and -54 respectively.

–The NFC East was really awful: For the majority of the season the NFC East was poised to be the worst division in NFL History in terms of Win-Loss% however due to a few key wins at the end they avoided that notorious classification, in turn being the second worst division in NFL History. However, since I started this metric in the last three years, the NFC East is by far the worst in terms of Time Margin. The NFC East teams all finished below #20 as they ranked #21, #23, #26, and #27 respectively. The truly shocking aspect is the 7-9 Washington Football team (The division winners) finished #26 in the league.

–The Meteoric Rise of the Dolphins: Full disclosure, the Miami Dolphins are my favorite team as I was born in raised in Miami. However, I obviously did not construct my metric to favor them. With that being said the Dolphins had a league worst margin last year around -15, but turned it around for nearly a 20 point swing to finish #8 in the league this season. Although the team missed the playoffs it is clear that Brian Flores is changing the team, and constructed a squad that played at a clear playoff level of play throughout the year.

Full Excel Sheet:

Kobe Bryant: An Advanced Stats Memoriam

Basketball great, legend, and icon, Kobe Bryant tragically passed away last month in a helicopter crash. The death of Bryant and his 13-year-old daughter Gianna shook not only the basketball and sports world but the world at large regardless of any individual’s connection to the sport. Despite having touched everyone who has watched a Basketball game this millennium, we send out special condolences to all who knew Kobe and Gigi personally. Although Kobe Bryant and Gigi were robbed of their future, the lives they touched, the memories they bestowed on everyone, will live on for an eternity.

With all of that being said, this is a sports analytics website, and there probably exists a no more fitting way to honor his legacy from our end, by recapping his remarkable 20-year-career through the numbers produced by our original advanced metrics.

The following article showcases Bryant’s regular season-by season as well as playoff career numbers through the lens of our metrics. Although previous articles have described the metrics in detail, I will summarize them in short.

PCR Formula: Pro Con Rating, a metric that evaluates how well players are playing statistically on a per game basis. Assigning positive weights to pros (Good Box score stats) and negative weights to cons (Bad box score stats).

=((Pts)+(Reb)+(Ast x 1.4)+(Blks x 1.2)+(Steals x 1.1))-((TOs)+(Field Goals Missed)+(Free Throws Missed x .5)+(Fouls))/Games Played

Total PCR: Total Pro Con Rating, just the total PCR; not on a per-game basis.

=PCR * Games Played

PCR/36: Pro Con Rating Per 36 Minutes, a way to evaluate how efficient players are given the pro-con rating metric.

=((Total PCR)/(Minutes Played))*36

EWC Formula:  Estimated Wins Contributed based on box score statistics.

=Player’s Total PCR/Team PCR)*Wins

EWC/82 Formula: Estimated Wins Contributed Per 82 Games, how many wins the player would have if they played 82 games based on their rate and statistics.

=(EWC/Games Played)*82

EWC/48 Formula: Estimated Wins Contributed Per 48 Minutes, an efficiency metric evaluating how many wins per 48 minutes a specific player is estimated to contribute.

=(EWC/Minutes Played)*48

In addition, I have included two metrics that I have not used on the site before, but are based in the above metrics.

PCR/100: PCR per 100 possesions. The same PCR formula but applied to https://www.basketball-reference.com/ Per 100 possessions statistics. PCR/100 is probably the best version of PCR to compare seasons and players from across eras if the players garnered similar amounts of playing times.

Possesions are Calculated as follows (Pulled from B-Ref’s site)=

 0.5 * ((Tm FGA + 0.4 * Tm FTA – 1.07 * (Tm ORB / (Tm ORB + Opp DRB)) * (Tm FGA – Tm FG) + Tm TOV) + (Opp FGA + 0.4 * Opp FTA – 1.07 * (Opp ORB / (Opp ORB + Tm DRB)) * (Opp FGA – Opp FG) + Opp TOV))

EWC/100:  Estimated Wins Contributed per 100 possesions.  Similar logic applies here as this is the number of wins a player contributes per 100 of their team’s possessions. The shorthand way to calculate EWC/100 is below. Points can be substituted for any box statistic that is measured in PCR/100. Take note that the Total Points * 100 divided by Pts Per 100 yields the number of possessions for a player.

=(EWC/((Total Points * 100)/Pts Per 100))*100

Kobe Bryant Career Advanced Stats Profile: 

Regular Season: (PPG/RPG/APG/SPG/BPG/FG%/3P%/FT%)
*Career Highs (Given the individual season qualifies)

1996-1997: 7.6/1.9/1.3/0.7/0.3/41.7%/37.5%/81.9%
Total PCR: 404.9
PCR: 5.0
PCR/36: 13.22
PCR/100: 19.17
EWC: 2.59
EWC/82: 2.62
EWC/48: 0.113
EWC/100: 0.123

1997-1998: 15.4/3.1/2.5/0.9/0.5/42.8%/34.1%/79.4%
Total PCR: 964.0
PCR: 12.2
PCR/36: 16.88
PCR100: 24.03
EWC: 6.16
EWC/82: 6.39
EWC/48: 0.144
EWC/100: 0.153

1998-1999: 19.9/5.3/3.8/1.4/1.0*/46.5%/26.7%/83.9%
Total PCR: 914.7
PCR: 18.29
PCR/36: 17.37
PCR/100: 25.31
EWC: 5.63
EWC/82: 9.23
EWC/48: 0.143
EWC/100: 0.155

1999-2000: 22.5/6.3/4.9/1.6/0.9/46.8%/31.9%/82.1%
Total PCR: 1477.2
PCR: 22.38
PCR/36: 21.06
PCR/100: 30.27
EWC: 10.90
EWC/82: 13.54
EWC/48: 0.207
EWC/100: 0.222

2000-2001: 28.5/5.9/5.0/1.7/0.6/46.4%/30.5%/85.3%
Total PCR: 1695.2
PCR: 24.93
PCR/36: 21.93
PCR/100: 31.83
EWC: 10.83
EWC/82: 13.06
EWC/48: 0.187
EWC/100: 0.203

2001-2002:25.2/5.5/5.5/1.5/0.4/46.9%*/25.0%/82.9%
Total PCR: 1895.5
PCR: 23.69
PCR/36: 22.28
PCR/100: 32.27
EWC: 12.28
EWC/82: 12.59
EWC/48: 0.192
EWC/100: 0.209

2002-2003: 30.0/6.9*/5.9/2.2*/0.8/45.1%/38.3%*/84.3%
Total PCR: 2359.9*
PCR: 28.78*
PCR/36: 24.98*
PCR/100: 35.95*
EWC: 13.58
EWC/82: 13.58
EWC/48: 0.192
EWC/100: .207

2003-2004: 24.0/5.5/5.1/1.7/0.4/43.8%/32.7%/85.2%
Total PCR: 1486.3
PCR: 22.87
PCR/36: 21.87
PCR/100: 31.76
EWC: 9.62
EWC/82: 12.14
EWC/48: 0.189
EWC/100: 0.205

2004-2005: 27.6/5.9/6.0*/1.3/0.8/43.3%/33.9%/81.6%
Total PCR: 1670.4
PCR: 25.31
PCR/36: 22.36
PCR/100: 32.84
EWC: 7.10
EWC/82: 8.82
EWC/48: 0.127
EWC/100: 0.139

2005-2006: 35.4*/5.3/4.5/1.8/0.4/45.0%/34.7%/85.0%
Total PCR: 2219.2
PCR: 27.74
PCR/36: 24.38
PCR/100: 35.73
EWC: 12.15
EWC/82: 12.45
EWC/48: 0.178
EWC/100: 0.196

2006-2007:31.6/5.7/5.4/1.4/0.5/46.3%/34.4%/86.8%*
Total PCR: 2158.0
PCR: 28.03
PCR/36: 24.74
PCR/100: 35.13
EWC: 10.53
EWC/82: 11.21
EWC/48: 0.161
EWC/100: 0.172

2007-2008: 28.3/6.3/5.4/1.8/0.5/45.9%/36.1%/84.0%
Total PCR: 2213.0
PCR: 26.99
PCR/36: 24.96
PCR/100: 34.67
EWC: 12.75
EWC/82: 12.75
EWC/48: 0.192
EWC/100: 0.200

2008-2009: 26.8/5.2/4.9/1.5/0.5/46.7%/35.1%/85.6%
Total PCR: 2013.5
PCR: 24.55
PCR/36: 24.49
PCR/100: 34.69
EWC: 13.60*
EWC/82: 13.60*
EWC/48: 0.221*
EWC/100: 0.234*

2009-2010: 27.0/5.4/5.0/1.5/0.3/45.6%/32.9%/81.1%
Total PCR: 1696.3
PCR: 23.24
PCR/36: 21.54
PCR/100: 31.02
EWC: 10.87
EWC/82: 12.21
EWC/48: 0.184
EWC/100: 0.198

2010-2011: 25.3/5.1/4.7/1.2/0.1/45.1%/32.3%/82.8%
Total PCR: 1799.5
PCR: 21.95
PCR/36: 23.31
PCR/100: 34.34
EWC: 11.22
EWC/82: 11.22
EWC/48: 0.194
EWC/100: 0.214

2011-2012: 27.9/5.4/4.6/1.2/0.3/43.0%/30.3%/84.5%
Total PCR: 1290.1
PCR: 22.24
PCR/36: 20.81
PCR/100: 30.66
EWC: 7.33
EWC/82: 10.36
EWC/48: 0.158
EWC/100: 0.174

2012-2013: 27.3/5.6/6.0/1.4/0.3/46.3%/32.4%/83.9%
Total PCR: 2001.7
PCR: 25.66
PCR/36: 23.92
PCR/100: 33.87
EWC: 9.90
EWC/82: 10.41
EW/48: 0.158
EWC/100: 0.167

2013-2014: 13.8/4.3/6.3/1.2/0.2/42.5%/18.8%/85.7%
Total PCR: 84.6
PCR: 14.1
PCR/36: 17.21
PCR/100: 23.06
EWC: 0.26
EWC/82: 3.55
EWC/48: 0.071
EWC/100: 0.071

2014-2015: 22.3/5.7/5.6/1.3/0.2/37.3%/29.3%/81.3%
Total PCR: 654.4
PCR: 18.7
PCR/36: 19.52
PCR/100: 27.63
EWC: 1.70
EWC/82: 3.98
EWC/48: 0.068
EWC/100: 0.072

2015-2016: 17.6/3.7/2.8/0.9/0.2/35.8%/28.5%/82.6%
Total PCR: 765.9
PCR: 11.6
PCR/36: 14.80
PCR/100: 20.80
EWC: 1.73
EWC/82: 2.15
EWC/48: 0.045
EWC/100: 0.047

Career: 25.0/5.2/4.7/1.4/0.5/44.7%/32.9%/83.7%
Total PCR: 29,764.3
PCR: 21.95
PCR/36: 22.03
PCR/100: 31.63
EWC: 170.73
EWC/82: 10.32
EWC/48: 0.168
EWC/100: 0.182

Playoffs: (Spreadsheet Format) (Includes spreadsheet format for regular season)

Key Takeawys:

-(Will be added within the week)

 

Week 14 NFL Time-Margin Team Ratings and Spreads

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Last Year I discussed my TIMEMAT Rating System, which was based in matrix and linear algebra. In this rating system, I decided to focus on the amount of time it took football teams to put a game away rather than more contemporary predictive measures such as point differential.

I will not go into detail regarding the system since I have already in the aforementioned article, however, I will give a quick rundown. The rating system revolves around the moment in a game in which the victorious team went up by more than one possession and kept the score margin at two possessions or more for the rest of the game. The idea may be better illustrated with an example:

Screenshot 2019-12-05 18.46.41.png

For example in the Packers 31-13 win over the Giants this past week, Green Bay received a 17.18 rating, because they put the game away with 14 minutes left in the game. As the 14 minute mark in the 4th quarter was the moment in which the opposing team (the Giants) failed to have a single possession in which they were within one possession of the Packers for the remainder of the game. The extra 3.18 is due to a home-field adjustment in which away teams are given an extra 3.18 while home teams are subtracted 3.18. This number is based on a linear regression equation relating point differential to my Time Differential, and commonly accepted standards of home-field advantage in the NFL: y=1.31x+0.15

If it is not clear yet what the metric gets after, the metric just calculates the amount of time left in the game when the victorious team put the game away for good and did not let the opposing team get back into the game. That is the goal of my metric.

There are some other facets to keep in mind, any game that is put away within the last 5 minutes of a game is assigned a Time Margin of 5.0. This goes for games that finish in a one-possession game as well.

That concludes the basic run-down of the backbone of my Time Margin Rating which is based on my TIMEMAT rating.

Time Margin Rating System:

My Time Margin Rating System is basically a synthesis of the common adjusted margin of victory rating systems such as SRS, and my TIMEMAT System. Unlike other systems I have done, this one is primarily a predictive measure. The process for calculating these ratings are relatively simple.

The formula is as follows:

Team A’s Time Margin: (1/2 *(Team A Mean Raw Time Margin+Team A Median Raw Time Margin)) + (1.25*(1/2* (Median Opponent Time Margin+Mean Opponent Time Margin)

Therefore my metric calculates the team’s raw time margin without accounting for strength of schedule then accounts for the strength of schedule accordingly.  I use a combination of the two most common measurements of central tendency (Mean and Median) to adjust for outliers but also to account for the fact that a high proportion of NFL Games end within one score.

The coefficient of 1.25 for the strength of schedule comes from the fact that without the coefficient:

STDEV of Team’s Time Margin: 7.36

STDEV of Team’s Time Margin SOS: 1.48

The lack of deviation causes the metric to discount the strength of schedule and make it nearly a nonfactor in my rating system. However, i multiply it by 1.25 which changed the ratio of Standard Deviations. In turn, it made it so that the approximate importance of the Team’s time margin was ~ 80%, while SOS was ~20%. This is more in turn with what I personally would stress importance on. In a league as full of parity as the NFL it makes sense that 80% of what matters is how good you look while 20% of what matters is who you do it against. This distribution of importance would, of course, be different in college football where the talent gap is much wider, and strength of schedule needs even more stressed importance.

Usually I discuss the pros and cons of my rating system, however, the same pros and cons for my TIMEMAT System apply here.

Calculating Spreads:

You can use my metric to calculate point spreads as well. I will list the point spreads for all week 14 games at the end of an article, but to exemplify how one can do it I will use the Dolphins @ Jets game this Sunday.

The Dolphins currently have a league-worst -15.02 in my metric while the Jets have the second-worst at -11.75. The Jets are at home for this game, therefore, they should have an inherent 3.18-time margin advance. So the calculations are as follows:

Differential= (-11.75+3.18)-(-15.02)=6.45

In Time Margin lingo this means the Jets should put the Dolphins Game away with 6.45 minutes left. If you wanted to convert this to a point spread, you simply plug it into the aforementioned Least Squares Equation* and do some algebra to find X:

6.45=1.31x+0.15

6.30=1.31X

6.3/1.31= 4.8

Therefore, the Jets are favored by 4.8 points against the Dolphins in my Metric.

*This equation is preliminary and is going to be adjusted for in the future to be more accurate  (Article will be updated at that time to reflect that)

Week 14 Ratings:

  1. New England Patriots (10-2): 14.09
  2. Baltimore Ravens (10-2): 13.52
  3. San Francisco 49ers (10-2): 12.08
  4. Kansas City Chiefs (8-4): 7.40
  5. Minnesota Vikings (8-4): 7.39
  6. Seattle Seahawks (10-2): 6.30
  7. Los Angeles Rams (7-5): 5.12
  8. New Orleans Saints (10-2): 4.70
  9. Green Bay Packers (9-3): 3.20
  10. Cleveland Browns (5-7): 2.27
  11. Houston Texans (8-4): 2.05
  12. Dallas Cowboys (6-6): 1.60
  13. Pittsburgh Steelers (7-5): 1.50
  14. Buffalo Bills (9-3): 0.74
  15. Philadelphia Eagles (5-7): 0.60
  16. Chicago Bears (6-6): 0.09
  17. San Diego Chargers (4-8): -0.79
  18. Indianapolis Colts (6-6): -.95
  19. Tennessee Titans (7-5): -1.02
  20. Tampa Bay Buccaneers (5-7): -1.23
  21. Detroit Lions (3-8-1): -1.85
  22. Carolina Panthers (5-7): -2.25
  23. Denver Broncos (4-8): -2.48
  24. Arizona Cardinals (3-8-1): -3.61
  25. New York Giants (2-10): -4.12
  26. Oakland Raiders (6-6): -5.41
  27. Atlanta Falcons (3-9): -5.54
  28. Jacksonville Jaguars (4-8): -6.69
  29. Cincinnati Bengals (1-11): -6.75
  30. Washington Redskins (3-9): -9.89
  31. New York Jets (4-8): -11.75
  32. Miami Dolphins (3-9): -15.02

Lines for Week 14 Games:

Cowboys @ Bears: Bears -1.2

Dolphins @ Jets: Jets -4.8

Panthers @ Falcons: Panthers -.2 (Pick Em)

Ravens @ Bills: Ravens -7.2

Bengals @ Browns: Browns -9.2

Redskins @ Packers: Packers -12.3

Lions @ Vikings: Vikings -9.4

49ers @ Saints: 49ers -3.1

Colts @ Buccaneers: Buccaneers -2.1

Broncos @ Texans: Texans -5.8

Chargers @ Jaguars: Chargers -2.0

Titans @ Raiders: Titans -.8

Chiefs @ Patriots: Patriots -7.4

Steelers @ Cardinals: Steelers -1.4

Seahawks @ Rams: Rams -1.4

 

Here is a spreadsheet with the ratings as well as strength of schedule for each NFL Team:

 

Introducing a New Way to Rate Quarterbacks: Quarterback Performance Value (QPV)

BBOV3rg.jpg

 

In the past, football has lagged behind its peers in terms of advanced statistics. However, it has recently caught up in some regards with the emergence of next-generation NFL Statistics. Advanced metrics ranging from ESPN’s QBR, Air Yards, Completion Probability, and speed measurements for ball carriers have been making gains in popularity and notoriety in recent seasons.

Consequently, there are now several different measurements and rating systems that try to evaluate the strength of a quarterback’s season or game. I have decided to create my own which I dub Quarterback Performance Value. Before I delve into the details regarding my system, I will discuss a few of the existing quarterback ratings that have all influenced mine as well.

NFL’s Passer Rating:

The oldest and most famous quarterback rating metric is the NFL’s Passer Rating. The Passer Rating formula was adopted by the NFL in 1973 due to then-commissioner Pete Rozelle asking the statistical community to develop a better system to determine the NFL’s best quarterback.  The formula is as follows:

Screenshot 2019-09-09 11.12.48

The NFL formula maxes out a 158.3 and bottoms out at 0.0. In addition to that, there are a few other positive and negative takeaways from this system. One pro of the system is that the formula is relatively simple and transparent. Another one is that there is no doubt a high relation between the statistic and winning football games as in 2010 teams that posted a higher passer rating went 205-53 (79.3%). One facet of the rating system that could be either a pro or con is the fact that it is entirely based on efficiency. Each statistic (completions, yards, TDs, and Ints) are all measured in relation to the amount of attempts a quarterback has. Additionally, the rating system does not take into account rushing stats, which is not reflective of the modern NFL in which making plays on your feet is gaining in importance for quarterbacks. Lastly the stat does not take into account things such as Wins or how much of the yards should be credited to the receiver or the quarterback.

ESPN’s Total QBR: 

As a whole, I find there to be more merit in ESPN’s Total QBR when compared to the more traditional passer rating.  This is due to the fact that more goes into the metric. ESPN’s official primer on it says

“QBR is built from the play level, it accounts for a team’s level of success or failure on every play to provide the proper context and then allocates credit to the quarterback and his teammate to produce a clearer measure of quarterback efficiency.”

However, the faults of the metric lie in the fact that there is not a whole lot of transparency. There is no formula available, and the exact way they measure and allocate credit is not published either.  More info on the metric can be found here.

Others:

PFF Football Focus Player grades– More subjective than other rating systems, however, provides more context than others.

Pro Football References Approximate Value– A method to rate all NFL players from every position.

My method:

My method uses a plethora of different statistics that are both efficiency-based and volume-based to compute an overall season rating for a quarterback’s performance. My rating system can also be used to measure a quarterback’s single-game performance, given a couple statistical adjustments.  Overall, I combined both subjective and objective facets from many areas of a quarterback’s game into one rating. The subjective aspects come from the linear weights given to each statistic, which were derived both from their importance as well as a nit-picking process in which I tried to yield the most reasonable results.

There are two main facets of my rating system: a volume rating and an efficiency rating. I will dive into each in-depth:

Part 1: Volume Rating

Volume rating is based purely on total statistics and does not take into account the efficiency of a quarterback. This is where I differ from many other rating systems. I give importance to pure volume statistics because there is a certain benefit in quarterbacks who can take a high workload and put out a lot of gaudy numbers. A quarterback who completes 80% of his passes on 25 attempts is much more impressive than one who completes 80% of his passes on only 10 attempts.

Now, with that being said, here is the formula for the volume-based metric:

Volume Rating= (Wins*6.5)+(Comletions/12)+(Incompletions/–10)+(Passing Yards/50)+(Passing Touchdowns*3.25)+(Interceptions*–1.65)+(Sacks*–0.25)+(4th Quarter Comebacks*2.5)+(Game winning Drives*2.5)+(Rush Yards/30)+(Rushing Touchdowns*1)+(Fumbles*–1.5)+(Losses*–4.5)+(Passing First Downs/10)+(Air Yards/30)+(Drops/3)+(Rushing First Downs*0.25)

Most of the aforementioned statistics involved in the volume are pretty self-explanatory if you are someone who follows football. However, there are a couple that may need defining. A 4th quarter comeback is considered to be an offensive scoring drive in the 4th quarter, in which the QB’s team was trailing by at least one score, though not necessarily a drive to take the lead. Obviously, only games ending in a win or tie are included. A game-winning drive is an offensive scoring drive in the 4th quarter or overtime that puts the winning team ahead for the last time. I included these statistics to account for clutchness in quarterbacks.

Air Yards is one of my favorite metrics for Quarterbacks as it is a way to divide credit for the big passing plays between Quarterbacks and Receivers. It is defined as the total yards passes traveled in the air from the line of scrimmage. In other words, it is the difference between passing yards and yards after the catch. One may question my inclusion of drops as a metric to measure a quarterback. My rationale behind this is that the inclusion accounts for how strong or weak a quarterback’s receivers were. If a quarterback has a poor receiving group, there may be a reason to think that it negatively impacted his stats.

Lastly, I will explain some rationale behind the linear weights given to each statistic. Higher linear weights for statistics do not necessarily correspond to a heightened importance. Some statistics are given a higher linear weight to account for a lack of deviation in the statistic for quarterbacks. Each passing yard has the same effect as each rushing yard however there is less range and deviation among rushing yards for quarterbacks, therefore I gave a higher weight to rushing yards.

Here was the Top 10 Leaders in my Volume Metric for the 2018 Season: 

  1. Patrick Mahomes Chiefs: 438.17
  2. Drew Brees Saints: 396.36
  3. Andrew Luck Colts: 379.53
  4. Jared Goff Rams: 374.23
  5. Ben Roethlisberger Steelers: 350.43
  6. Phillip Rivers Chargers: 349.49
  7. Deshaun Watson Texans: 348.58
  8. Matt Ryan Falcons: 341.30
  9. Tom Brady Patriots: 329.17
  10. Russell Wilson Sehawks: 313.69

The volume metric on it’s own can be a good measurement of a single quarterback’s single-season performance just based on this top ten. However, the necessity in including the efficiency metric as well can be seen by the fact that Carson Wentz ranked #20 in my volume metric last year while Eli Manning tallied in at #17. There is a little to no corroboration of the belief that Eli Manning is a better quarterback than Carson Wentz last year.

Part 2: Efficiency Rating 

The efficiency rating uses many of the same statistics as the volume stats but adjusts them for the number of passing attempts or rushing attempts by a given quarterback. One aspect to be aware of is that all rate statistics (Completion %, W-L%, TD%, ETC) are all in their percentage forms. So, therefore, a quarterback who completes 300/500 passes in a season is given a 60% rather than the decimal .6. The formula for the Efficiency Rating is:

Efficency=(Win %/3)+(Completion %/3.25)+(Yards Per Pass Attempt*4.5)+(Passing TD%*4.9)+(Interception%*–4.35)+(Sack %*–1.5)+(Average Rush*2)+(Rush TD%/3)+(Pass 1st Down %/20)+(Air Yards Per Attempt*4.25)+(Drop %*1.4)+(Rush 1st Down%/6)

Again, most of these statistics are pretty self-explanatory however, some have more notoriety than others. Passing TD% and Int% are simply TDs/Ints divided by the number of attempts. Sack% is Times Sacked/(Pass Attempts + Times Sacked). The First Down % statistics are passing/rushing first downs divided by passing/rushing attempts.

Again, the given statistical weight does not correspond to my perceived importance or even the statistical impact of the stat. Obviously, W-L% is more important than Drop % however given certain inherent statistical qualities of the two metrics, Drop % is given a higher weight. This is because Drop %’s are inherently lower than W-L%’s.

Here is the Top Ten for my efficiency metric for the 2018 regular season: 

  1. Drew Brees Saints: 154.67
  2. Patrick Mahomes Chiefs: 153.32
  3. Russell Wilson Seahawks: 144.28
  4. Matt Ryan Falcons: 140.30
  5. Andrew Luck Colts: 134.30
  6. Mitch Tribuisky Bears:  133.78
  7. Jared Goff Rams: 132.69
  8. Tom Brady Patriots: 131.23
  9. Deshaun Watson Texans: 130.98
  10. Phillip Rivers Chargers:  129.17

As was the case with the volume metric, the efficecny metric yields a pretty reasonable top ten. However within the efficency metric the 11th rated player isn’t Ben Roethlisberger or Aaron Rodgers, it’s Ryan Fitzpatrick yielding a rating of 128.55. Fitzmagic did provide a few stellar performances in the regular season he by no means was one of the 11 best quarterbacks in the 2018 regular season.

Part 3: Putting it into one Value 

Lastly, I combined these two metrics into one singular value by doing the following:

(Volume Rating/2.25)+(Efficiency Rating * 1.6)

___________________________________________

                            2.2

Based on my results for the 2018 season I tried to lay out some approximate guidelines for what each rating means. These will be subject to change as I apply the metric to subsequent and previous seasons.

Quarterback Performance Value Rating Correspondance: 

195.00+: All-Time Great QB Season 

180-195: MVP Level Season 

170-180: Possible MVP Candidate/Top 5 QB in the League 

160-170: Pro Bowl Season

150-160: Border-line Pro Bowl Season 

140-150: Solid QB Season 

127.5-140: Average Starter 

115-127.5: Sub Par Starter 

110-115: Bad Starter 

<110: Probably Should Look into Getting a New Quarterback 

Here is the leaderboard for the past season;

Top 32 Quarterbacks for the 2018 Regular Season:

  1. Patrick Mahomes Chiefs:  200.0
  2. Drew Brees Saints: 192.6
  3. Andrew Luck Colts: 174.3
  4. Jared Goff Rams: 172.1
  5. Matt Ryan Falcons: 171.0
  6. Russell Wilson Seahawks: 168.3
  7. Deshaun Watson Texans: 165.7
  8. Phillip Rivers Chargers: 164.5
  9. Ben Roethlisberger Steelers: 162.1
  10. Tom Brady Patriots: 161.9
  11. Mitch Trubisky Bears: 155.1
  12. Aaron Rodgers Packers: 149.8
  13. Kirk Cousins Vikings: 147.1
  14. Dak Prescott Cowboys: 145.3
  15. Baker Mayfield Browns: 141.4
  16. Cam Newton Panthers: 135.0
  17. Carson Wentz Eagles: 126.5
  18. Andy Datlon Bengals: 126.5
  19. Jameis Winston Buccaneers: 126.0
  20. Ryan Fitzpatrick Buccaneers: 122.2
  21. Eli Manning Giants: 122.2
  22. Case Kennum Broncos: 119.5
  23. Matthew Stafford Lions: 116.5
  24. Marcus Mariota Titans: 114.3
  25. Derek Carr Raiders:  113.0
  26. Nick Foles Eagles: 112.9
  27. Lamar Jackson Ravens: 112.2
  28. Joe Flacco Ravens:  111.1
  29. Alex Smith Redskins: 110.5
  30. Ryan Tannehill Dolphins: 105.1
  31. Josh Allen Bills: 103.6
  32. Sam Darnold Jets: 99.7

Here is my spreadsheet with all of the statistics and the ratings for every player who passed for over 150 attempts last year:1

 

What my metric does and what my metric does not do?: 

My metric does provide a solid measurement of a quarterback’s value in a given regular season. This is based on a plethora of different statistics and measurements that go into it in turn, giving it a wholesome view of a quarterback’s game. But it does not serve as a substitution for watching games and making judgments of quarterbacks plays given the eye test. Statistics are never meant to be used as a substitution for thoughtful analysis of the game, which can oftentimes only be done through watching the games themselves. Therefore my metric works based when used in a complimentary way.

 

 

 

Here are the Top Tens from last year for the other metrics I discussed earlier in the article:

Passer Rating:

  1. Drew Brees Saints: 115.7
  2. Patrick Mahomes Chiefs: 113.8
  3. Russell Wilson Seahawks: 110.9
  4. Matt Ryan Falcons: 108.1
  5. Phillip Rivers Chargers: 105.5
  6. Deshaun Watson Texans: 103.1
  7. Carson Wentz Eagles: 102.2
  8. Jared Goff Rams: 101.1
  9. Ryan Fitzpatrick Buccaneers: 100.4
  10. Kirk Cousins Vikings: 99.7

ESPN Total QBR:

  1. Patrick Mahomes Chiefs: 80.3
  2. Drew Brees Saints:  79.2
  3. Mitch Trubisky Bears: 71.0
  4. Ben Roethlisberger Steelers: 69.6
  5. Andrew Luck Colts: 69.6
  6. Tom Brady Patriots: 68.4
  7. Phillip Rivers Chargers: 67.8
  8. Jameis Winston Buccaneers: 66.2
  9. Matt Ryan Falcons: 65.7
  10. Jared Goff Rams: 63.6

Approximate Value:

1. Patrick Mahomes Chiefs: 22

2. Jared Goff Rams: 18

3. Drew Brees Saints: 17

4. Matt Ryan Falcons and Deshaun Watson Texans: 16

6. Andrew Luck Colts: 15

7. Tom Brady Patriots, Dak Prescott Cowboys, Phillip Rivers Chargers, Ben Roethlisberger Steelers, and Russell Wilson Seahawks: 14

Pro Football Focus Player Grades:

  1. Drew Brees Saints: 94.7
  2. Patrick Mahomes Chiefs: 93.2
  3. Andrew Luck Colts: 91.1
  4. Phillip Rivers Chargers: 90.8
  5. Tom Brady Patriots: 90.7
  6. Aaron Rodgers Packers: 89.9
  7. Russell Wilson Seahawks: 87.2
  8. Jared Goff Rams: 85.5
  9. Baker Mayfield Browns: 84.5
  10. Matt Ryan Falcons: 84.3

Ranking the Best College Basketball Programs of the Decade Using an Original Formula

Duke-basketball-zion-williamson-kentucky.jpg

Photo Via: NCAA.Com

Believe it or not, depending on your idea of a decade, we are through with the 2010s of College Basketball. This is true if you base it off the latter part of a college basketball season since they span multiple calendar years. However, the second calendar year of the CBB season includes more regular-season games and March Madness. There have been 10 seasons if you start from the 2009-2010 season and end with the 2018-2019 season.

With that being said, I created a formula to rate the best college basketball programs from this 10-year span. The formula which I will show shortly includes a plethora of different markers for the success of a program. Including regular-season success (Wins, Final AP Rankings, Conference Regular Season Titles, etc), NCAA Tournament Success, and NBA Draft Success. I tried to give the most weight to postseason success, then the second most weight to the regular season facets, and the least weight to NBA Draft success. This is due to the fact that NBA Draft picks aren’t necessarily indicative of how good the program has been, however, one of the goals of a college basketball program is to produce players ready for the next level.

The formula is as follows:
Program Success=
(Total Wins*3)+(W-L%*3.5)+(NCAA Tournament Wins*15)+(AP Top 5 Finishes*100)+(AP Top 10 Finishes*50)+(AP Top 15 Finishes*25)+(AP Top 25 Finishes*15)+(#1 Seeds*100)+(Top 4 Seeds*50)+(NCAA Tournament Apps*10)+(NCAA Titles*500)+(NCAA Title Game Apps*200)+(Final Fours*175)+(Elite 8s*125)+(Sweet 16s*100)+(Conf Tournament Titles*30)+(Conf Regular Season Titles*45)+(Top 5 NBA Draft Picks*40)+(Lottery Picks*25)+(First Round Picks*15)+(NBA Draft Picks*5)

For simplicity’s sake, I only calculated the Program Success Formula for 60 programs that either met certain qualifiers or ones that I saw as being moderately successful this decade. The formula yielded the subsequent Top 25:
Key:
Strongest Stats – The stats in which the program had the highest relative rank in.
All-Decade Starting 5- The best individual seasons from players on the team this decade (Limit of one per player). There is an emphasis on overall greatness, so if two players have a similar best season the one who had a better overall career is chosen.
Best Team– The Best team from the program this decade based on both regular season success and tournament success. There is an emphasis on the tournament success aspect.

1. Kentucky Wildcats SEC: 8508.85
Strongest Stats: #1 in Wins, Tournament Wins, Title Game Apps, Final 4s, Elite 8s, Sweet 16s, Lottery Picks, 1st Round Picks, NBA Draft Picks
All Decade Starting 5:
C- Anthony Davis 2011-2012
F- Willie Cauley-Stein 2014-2015
F- Demarcus Cousins 2009-2010
G- John Wall 2009-2010
G- Tyler Ulis 2015-2016
6th Man: Karl-Anthony Towns 2014-2015
Best Team: 2011-2012 38-2 AP: #1 Won National Final

2. Duke Blue Devils ACC: 8100.50
Strongest Stats: #1 In AP Top 10s, AP Top 15s, AP Top 25s, Top 4 Seeds, Tournament Apps, NCAA Titles, Title Game Apps, Top 5 Picks
All Decade Starting 5:
C- Jahlil Okafor 2014-2015
F- Marvin BAgley III 2017-2018
F- Zion Williamson 2018-2019
G- R.J. Barrett 2018-2019
G- Nolan Smith 2010-2011
6th Man: Jabari Parker 2013-2014
Best Team: 2014-2015 35-4 AP: #4 Won National Final

3. Kansas Jayhawks Big 12: 7134.70
Strongest Stats: #1 AP Top 5s, AP Top 10s, AP Top 15s, AP Top 25s, #1 Seeds, Top 4 Seeds, Tournament Apps, Conf Regular Season Titles
All Decade Starting 5:
C- Jeff Whitey 2012-2013
F- Thomas Robinson 2011-2012
F- Perry Ellis 2015-2016
G- Frank Mason 2016-2017
G- Sheron Collins 2009-2010
6th Man: Andrew Wiggins 2013-2014
Best Team: 2011-2012 32-7 AP: #6 Lost National Final

4. North Carolina Tar Heels ACC: 6211.05
Strongest Stats: #1 in Title Game Apps
All Decade Starting 5:
C- Tyler Zeller 2011-2012
F- Brice Johnson 2015-2016
F- Justin Jackson 2016-2017
G- Marcus Paige 2013-2014
G- Kendall Marshall 2011-2012
6th Man: Luke Maye 2017-2018
Best Team: 2016-2017 33-7 AP: #6 Won National Final

5. Villanova Wildcats Big East: 5542.05
Strongest Stats: #1 in NCAA Titles and Title Game Apps
All Decade Starting 5:
F- Mikal Bridges 2017-2018
F- Kris Jenkins 2015-2016
G- Jalen Brunson 2017-2018
G- Josh Hart 2016-2017
G- Scottie Reynolds 2009-2010
6th Man: Donte Divincenzo 2017-2018
Best Team: 2017-2018 36-4 AP: #2 Won National Final

6. Gonzaga Bulldogs WCC: 4997.40
Strongest Stats: #1 in W-L%, Tournament Apps, Conf Regular Season Titles
All Decade Starting 5:
C- Kelly Olynk 2012-2013
F- Rui Hachimura 2018-2019
F- Brandon Clarke 2018-2019
G- Nigel Williams-Goss 2016-2017
G- Kevin Pangos 2014-2015
6th Man: Kyle Wiltjer 2014-2015
Best Team: 2016-2017 37-2 AP: #2 Lost National Final

7. Michigan State Spartans Big 10: 4984.00
Strongest Stats: #1 in Tournament Apps
All Decade Starting 5:
F- Adreian Payne 2013-2014
F- Draymond Green 2011-2012
G- Denzel Valentine 2015-2016
G- Cassius Winston 2018-2019
G- Miles Bridges 2017-2018
6th Man: Gary Harris 2013-2014
Best Team: 2018-2019 32-7 AP: #5 Lost in Final 4

8. Virginia Cavaliers ACC: 4616.35
Strongest Stats: #2 in AP Top 5 Finishes and 1 Seeds
All Decade Starting 5:
F- Mike Scott 2011-2012
F- De’Andre Hunter 2018-2019
G- Malcolm Brogdon 2015-2016
G- Kyle Guy 2018-2019
G- Ty Jerome 2018-2019
6th Man: Joe Harris 2012-2013
Best Team: 2018-2019 35-3 AP: #2 Won National Final

9. Louisville Cardinals ACC 4417.75
Strongest Stats: #3 in Final 4 Apps
All Decade Starting 5:
F- Gorgui Dieng 2012-2013
F- Montrezl Harrell 2014-2015
G- Russ Smith 2013-2014
G- Terry Rozier 2014-2015
G- Peyton Siva 2012-2013
6th Man: Donovan Mitchell 2016-2017
Best Team: 2012-2013 35-5 AP: #2 Won National Final

10. Michigan Wolverines Big 10: 4010.40
Strongest Stats: #1 in Title Game Apps
All Decade Starting 5:
F- Moritz Wagner 2017-2018
F- Glenn Robinson 2012-2013
G- Trey Burke 2012-2013
G- Nik Stauskas 2013-2014
G- Tim Hardaway Jr 2012-2013
6th Man: Manny Harris 2009-2010
Best Team: 2012-2013 31-8 AP: #10 Lost National Final

11. Syracuse Orange ACC: 3935.20
Strongest Stats: #3 in Final Four Apps
All Decade Starting 5:
F- Wes Johnson 2009-2010
F- C.J. Fair 2013-2014
F- Rakeem Christmas 2014-2015
G- Tyler Ennis 2013-2014
G- Michael Carter-Williams 2012-201
6th Man: Rick Jackson 2010-2011
Best Team: 2011-2012 34-3 AP: #2 Lost in Elite 8

12. Arizona Wildcats PAC 12: 3863.80
Strongest Stats: #4 in Top 5 Picks
All Decade Starting 5:
C- DeAndre Ayton 2017-2018
F- Derrick Williams 2010-2011
F- Lauri Markkanen 2016-2017
G- Nick Johnson 2013-2014
G- T.J. McConnell 2014-2015
6th Man: Aaron Gordon 2013-2014
Best Team: 2014-2015 34-4 AP: #5 Lost in Elite 8

13. UCONN Huskies AAC: 3673.2
Strongest Stats: #1 in NCAA Titles and Title Game Apps
All Decade Starting 5:
F- Andre Drummond 2011-2012
F- DeAndre Daniels 2013-2014
G- Kemba Walker 2010-2011
G- Shabazz Napier 2013-2014
G- Jeremy Lamb 2011-2012
6th Man: Ryan Boatright 2014-2015
Best Team: 2010-2011 32-9 AP: #9 Won National Final

14. Wisconsin Badgers Big 10: 3600.45
Strongest Stats: #3 in AP Top 25 Finishes, Final Fours, Sweet 16s
All Decade Starting 5:
F- Frank Kaminsky 2014-2015
F- Ethan Happ 2018-2019
F- Nigel Hayes 2014-2015
G/F- Sam Dekker 2014-2015
G- Jordan Taylor 2010-2011
6th Man: Jon Leuer 2010-2011
Best Team: 2014-2015 36-4 AP: #3 Lost National Final

15. Florida Gators SEC: 3507.75
Strongest Stats: #2 in Elite 8s
All Decade Starting 5:
F- Dorian Finney-Smith 2015-2016
F- Casey Prather 2013-2014
F- Chandler Parsons 2010-2011
G- Scottie Wilbekin 2013-2014
G- Bradley Beal 2011-2012
6th Man: Patric Young 2013-2014
Best Team: 2013-2014 36-3 AP: #1 Lost in Final 4

16. Ohio State Buckeyes Big 10: 3450.60
Strongest Stats: #5 in Top 5 picks
All Decade Starting 5:
F- Jared Sullinger 2010-2011
F- Keita Bates-Diop 2017-2018
F- Evan Turner 2009-2010
G- D’Angelo Russell 2014-2015
G- Aaron Craft 2012-2013
6th Man: Deshaun Thomas 2012-2013
Best Team: 2010-2011 34-3 AP: #1 Lost in Sweet 16

17. Butler Bulldogs Big East: 2854.70
Strongest Stats: #1 in Title Game Apps
All Decade Starting 5:
F- Matt Howard 2010-2011
F- Kelan Martin 2017-2018
F- Gordon Hayward 2009-2010
G- Shelvin Mack 2009-2010
G- Kamar Baldwin 2017-2018
6th Man: Rotnei Clarke 2012-2013
Best Team: 2009-2010 33-5 AP: #11 Lost National Final

18. Oregon Ducks PAC 12: 2800.20
Strongest Stats: #9 in 1 Seeds
All Decade Starting 5:
F- Jordan Bell 2016-2017
F- Chris Boucher 2015-2016
G- Dillon Brooks 2016-2017
G- Joe Young 2014-2015
G- Tyler Dorsey 2016-2017
6th Man: Troy Brown 2017-2018
Best Team: 2016-2017 33-6 AP: #9 Lost in Final 4

19. Wichita State Shockers AAC: 2620.10
Strongest Stats: #5 in Wins, W-L%, Conf Regular Season Titles
All Decade Starting 5:
F- Cleanthony Early 2013-2014
F- Carl Hall 2012-2013
G- Fred VanVleet 2013-2014
G- Ron Baker 2014-2015
G- Landry Shamet 2017-2018
6th Man: Tekele Cotton 2013-2014
Best Team: 2013-2014 35-1 AP: #2 Lost in Round of 32

20. Purdue Boiler Makes Big 10: 2464.10
Strongest Stats: #6 in AP Top 15 Finishes
All Decade Starting 5:
F- Robbie Hummel 2011-2012
F- Caleb Swanigan 2016-2017
F- JaJuan Johnson 2010-2011
G- Carsen Edwards 2018-2019
G- E’Twaun Moore 2010-2011
6th Man: AJ Hammons 2015-2016
Best Team: 2017-2018 30-7 AP: #11 Lost Sweet 16

21. Xavier Musketeers Big East: 2382.90
Strongest Stats: #9 in 1 Seeds
All Decade Starting 5:
F- Matt Stainbrook 2014-2015
F- Trevon Bluiett 2017-2018
G- Jordan Crawford 2009-2010
G- Tu Holloway 2010-2011
G- Semaj Christon 2013-2014
G- Edmond Sumner 2016-2017
6th Man: J.P. Macura 2017-2018
Best Team: 2017-2018 29-6 AP: #3 Lost in Round of 32

22. Baylor Bears Big 12: 2352.25
Strongest Stats: #10 in AP Top 25s
All Decade Starting 5:
F- Jonathan Motley 2016-2017
F- Ekpe Udoh 2009-2010
F- Taurean Prince 2015-2016
G- Pierre Jackson 2012-2013
G- LaceDarius Dunn 2009-2010
6th Man: Rico Gathers 2014-2015
Best Team: 2011-2012 30-8 AP: #9 Lost in Elite 8

23. West Virginia Mountaineers Big 12: 2304.55
Strongest Stats: #10 in AP Top 25s
All Decade Starting 5:
F- Da’Sean Butler 2009-2010
F- Kevin Jones 2011-2012
F- Devin Williams 2015-2016
G- Jevon Carter 2017-2018
G- Juwan Staten 2013-2014
6th Man: Devin Ebanks 2009-2010
Best Team: 2009-2010 31-7 AP: #6 Lost in Final 4

24. Notre Dame Fighting Irish ACC: 2056.60
Strongest Stats: #12 in Elite 8s
All Decade Starting 5:
F- Luke Harangody 2009-2010
F- Bonzie Colson 2016-2017
G- Jerian Grant 2014-2015
G- Ben Hansbrough 2010-2011
G- Demetrius Jackson 2015-2016
6th Man: Zach Auguste 2015-2016
Best Team: 2014-2015 32-6 AP: #8 Lost in Elite 8.

25. Indiana Hoosiers Big 10: 2038.00
Strongest Stats: #5 in Top 5 Picks
All Decade Starting 5:
F- Cody Zeller 2012-2103
F- Christian Watford 2012-2013
F-Juwan Morgan 2018-2019
G- Victor Oladipo 2012-2013
G- Yogi Ferrell 2015-2016
6th Man: Noah Vonleh 2013-2014
Best Team: 2012-2013 29-7 AP: #4 Lost in Sweet 16

Here is the full spreadsheet of the 60 teams that I calculated ratings for:

Decade Tables: Which soccer clubs/countries have been the most successful this decade?

 

Photo Via: The Independent 

Unbelievably this current decade is coming to an end, and next year’s top soccer competitions and leagues will have a majority of their matches take place in 2020 rather than the 2019 calendar year. Therefore it seems like a good time to recap the 2010-2019 decade of soccer. This decade had a plethora of storylines and trends that impacted the world of soccer such as Manchester United’s decline, Manchester City’s rise, and Real Madrid’s European dominance.

This article will firstly show the decade tables for the Big 5 European Leagues; English Premier League, Spanish La Liga, Italian Serie A, German Bundesliga, and the French Ligue 1. In this, I tallied up the points and goal differential attained by each club in top-flight matches starting from the 2009-2010 season and finishing up with this past 2018-2019 season. Additionally, I did this for two other competitions: the UEFA Champions League and FIFA World Cup. The results are below:

British Premier League Decade Table: 

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  1. Manchester City Pts: 812 GD: 483
  2. Manchester United Pts: 759 GD: 334
  3. Chelsea Pts: 750 GD: 353
  4. Arsenal Pts: 719 GD: 300
  5. Tottenham Pts: 710 GD: 249
  6. Liverpool Pts: 688 GD: 293
  7. Everton Pts: 564 GD: 78
  8. Stoke City Pts: 412 GD: -110
  9. West Ham Pts: 402 GD: -94
  10. Newcastle Pts: 366 GD: -93
  11. West Bromwich Pts: 342 GD: -102
  12. Southampton Pts: 341 GD: -10
  13. Sunderland Pts: 314 GD: -128
  14. Swansea City Pts: 312 GD: -77
  15. Aston Villa Pts: 284 GD: -133
  16. Crystal Palace Pts: 269 GD: -56
  17. Leicester City Pts: 265 GD: 7
  18. Fulham Pts: 248 GD: -106
  19. Burnley Pts: 197 GD: -107
  20. Bournemouth Pts: 177 GD: -64

Takeaways:
–Manchester City’s Dominance: City have not only become the kings of Manchester, they have become the dominant club in England as well. As they are 53 points clear of second place United.
–Arsenal’s Consistency: Although this decade has seen the Gunners end their Champions League Streak, they have been the most consistent club in England. Their Points standard deviation of 4.51 was by far the lowest of any club in the league.
–Leicester’s 2015-2016 Run: There is little to be said about Leicester’s remarkable title-winning season that has not already been said. However, when put in the context of the decade, it seems even more remarkable. The other clubs to have won a BPL title all rank within the top 3, while the foxes fall at 17th in the table given the fact they were not in top flight for the first half of the decade, and their second best point tally falls 29 points below their 81 point 2016 season.

Spanish La Liga Decade Table: 

logotipos2.jpg

  1. Barcelona Pts: 928 GD: 750
  2. Real Madrid Pts: 879 GD: 660
  3. Atletico Madrid Pts: 726 GD: 285
  4. Valencia Pts: 618 GD: 135
  5. Sevilla Pts: 601 GD: 92
  6. Athletic Bilbao Pts: 552 GD: 16
  7. Villarreal Pts: 514 GD: 60
  8. Espanyol Pts: 475 GD: -102
  9. Real Sociedad Pts: 474 GD: 7
  10. Malaga Pts: 407 GD: -69
  11. Getafe Pts: 385 GD: -84
  12. Levante Pts: 353 GD: -119
  13. Celta Vigo Pts: 332 GD: -51
  14. Real Betis Pts: 322 GD: -100
  15. Deportivo La Coruna Pts: 267 GD: -150
  16. Rayo Vallecano Pts: 258 GD: -142
  17. Osasuna Pts: 244 GD: -128
  18. Eibar Pts: 230 GD: -38
  19. Granada Pts: 219 GD: -172
  20. Mallorca Pts: 194 GD: -33

Takeaways:
–Barca’s League Dominance: While Barcelona’s archnemesis, Real Madrid, has emphatically dominated Europe, Barca has owned La Liga. They have won 7/10 League titles, and tallied up 928 points. The 928 points is higher than any club’s decade league points within the big five leagues.
–Mallorca’s Surprising Inclusion: Mallorca has not been in Spanish top flight for six straight seasons. The club tied for the 20th and last spot in the table with Sporting Gijon with 194 pts, but held a superior goal differential.
–The Truth of La Liga’s Top Heavy Perception: The Spanish league has often been dubbed as a top-heavy league, and a league that is usually Europe’s best when comparing their very best to other league’s very best clubs. This trend is seen in the league table as there is a 108 point gap between 3rd Place Atletico Madrid and 4th place Valencia.

Italian Serie A Decade Table: 

lega-serie-a-istituti-professionali

  1. Juventus Pts: 840 GD: 416
  2. Napoli Pts: 747 GD: 327
  3. Roma Pts: 726 GD: 270
  4. AC Milan Pts: 665 GD: 196
  5. Inter Milan Pts: 655 GD: 198
  6. Lazio Pts: 605 GD: 136
  7. Fiorentina Pts: 565 GD: 98
  8. Udinese Pts: 492 GD: -39
  9. Atalanta Pts: 454 GD: 2
  10. Genoa Pts: 446 GD: -90
  11. Sampdoria Pts: 441 GD: -38
  12. Chievo Pts: 413 GD: -158
  13. Cagliari Pts: 379 GD: -224
  14. Bologna Pts: 374 GD: -118
  15. Torino: Pts: 365 GD: 29
  16. Parma Pts: 321 GD: -63
  17. Palermo Pts: 310 GD: -64
  18. Sassuolo Pts: 276 GD: -70
  19. Catania Pts: 227 GD: -46
  20. Empoli Pts: 158 GD: -66

Takeaways:
–Inter and AC Milan’s relative decline: From 2004-2011 the Serie A title was held by a Milan club, but AC Milan and Inter fall in at distant 4ths and 5ths respectively in the decade table. AC Milan has not finished in a Champions League qualifying position since the 2012-2013 season.
–Atalanta’s Recent Run: Atalanta has had a three-year streak of 3+ 60 Pt years, highlighted by a third-place finish this past year. This streak has pushed them 9th in the decade table, while they were 14th in the first 7 years of the decade.
–The Inclusion of Roma and Napoli in the New Serie A Big 3: Within the historical context of the Serie A the big three is known to be Juventus, Milan and Inter as they have combined for more than 60% of All Serie A titles. However, within this decade there is a big gap between Roma’s third-place 726 points and Milan’s 4th place 665 points.

German Bundesliga Decade Table: 

1200px-Bundesliga_logo_(2017).svg

  1. Bayern Munich Pts: 800 GD: 600
  2. Borussia Dortmund Pts: 669 GD: 332
  3. Bayer Leverkusen Pts: 582 GD: 170
  4. Schalke 04 Pts: 527 GD: 81
  5. Monchenbladbach Pts: 505 GD: 51
  6. VfL Wolfsburg Pts: 474 GD: 10
  7. TSG 1899 Hoffenheim Pts: 450 GD: 15
  8. Mainz 05 Pts: 445 GD: -35
  9. Werder Bremen Pts: 438 GD: -59
  10. Eintracht Frankfurt Pts: 391 GD: -58
  11. VfB Stuttgart Pts: 373 GD: -84
  12. SC Freiburg Pts: 360 GD: -130
  13. Hannover 96 Pts: 360 GD: -136
  14. Hamburger SV Pts: 353 GD: -131
  15. FC Augsburg Pts: 321 GD: -80
  16. Hertha BSC Pts: 316 GD: -87
  17. FC Koln Pts: 266 GD: -96
  18. FC Nurnberg Pts: 209 GD: -118

Takeaways:
–RB Leipzig’s Controversial Rise falling short:
Leipzig has argubaly become the most hated club within the Bundesliga. Whether you love them or hate them the club has had a meteoric rise given the fact they have finished 2nd, 6th, and 3rd in their first three ever seasons in German Top Flight. The club would fall at 3rd in a table tallied up from just the past three years. However they fall one spot out of 18th in the past 10 years, despite having the fifth best Goal Differential of any club the past decade.
–Bayern rules Germany: Bayern Munich has won 8/10
Bundesliga Titles, and are on a current run of seven straight. Their dominance can also be seen by the 131 point gap that exists between them and second-place Dortmund. This gap is the highest gap between a 1st and 2nd place club of any of the big 5 decade tables.
–Lots of Parity within the Middle-Low Range of the Table:
The Bundesliga often gets a bad rep for a lack of parity, and much of this criticism is valid. However, the parity that exists at the middle and lower end of the table rivals that of any league. The gap between 7th place Hoffenheim’s 450 points and 15th Place Augsburg’s 321 points is practically the same as the gap between the 1st and 2nd place clubs.

French Ligue 1 Decade Table: 

Ligue-1

  1. PSG Pts: 808 GD: 497
  2. Lyon Pts: 682 GD: 253
  3. Marseille Pts: 642 GD: 176
  4. Lille Pts: 628 GD: 205
  5. Saint-Etienne Pts: 575 GD: 75
  6. Bordeaux Pts: 552 GD: 46
  7. Montpellier Pts: 546 GD: 33
  8. Nice Pts: 537 GD: -10
  9. Monaco Pts: 526 GD: 153
  10. Rennes Pts: 523 GD: 8
  11. Toulouse Pts: 454 GD: -73
  12. Lorient Pts: 373 GD: -53
  13. Caen Pts: 292 GD: -118
  14. Nantes Pts: 290 GD: -46
  15. Guingamp Pts: 259 GD: -89
  16. Reims Pts: 229 GD: -52
  17. Bastia Pts: 227 GD: -70
  18. Sochaux Pts: 222 GD: -67
  19. Valenciennes Pts: 220 GD: -38
  20. Nancy Pts: 214 GD: -65

Takeaways:
–Monaco’s Rise and Fall: Monaco had a near regulation season this past year, just two years removed from winning the league in 16/17. The club has had a remarkable recent run of success, racking up 70+ points in four out of the past six years. The club also garnered almost as many points (95) in the 16/17 season as they did in the first four years of the decade (99).
–Ligue 1’s Revolving Door of Relegation and Promotion: The French league has seen more teams in top flight this decade than any of the other top five leagues with 37. The league has also had 6 different clubs who have only appeared in one season of top-flight play, more than any other big league.
-Rennes’ non-Stellar Consistency: Rennes finished 10th in the decade league table despite being in top flight every year and only possessing two seasons in which they finished in the bottom half (12th and 13th). The club has had a run of consistent average play as they possess the lowest standard deviation of any French club (4.67).

UEFA Champions League Decade Table: 

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  1. Real Madrid Spain Pts: 259 GD: 168
  2. Barcelona Spain Pts: 240 GD: 145
  3. Bayern Munich Germany Pts: 229 GD: 136
  4. Juventus Italy Pts: 132 GD: 36
  5. Chelsea England Pts: 131 GD: 69
  6. Manchester United England Pts: 129 GD: 41
  7. PSG France Pts: 122 GD: 70
  8. Atletico Madrid Spain Pts: 120 GD: 36
  9. Porto Portugal Pts: 116 GD: 25
  10. Arsenal England Pts: 111 GD: 30
  11. Manchester City England Pts: 109 GD: 37
  12. Dortmund Germany Pts: 92 GD: 25
  13. Shakhtar Donetsk Ukraine Pts: 79 GD: -6
  14. Benfica Portugal Pts: 79 GD: -24
  15. Schalke 04 Germany Pts: 65 GD: -7
  16. Liverpool England Pts: 64 GD: 31
  17. Lyon France Pts: 63 GD: 2
  18. Inter Milan Italy Pts: 61 GD: 5
  19. Olympiacos Greece Pts: 60 GD: -19
  20. Tottenham England Pts: 59 GD: 14
  21. CSKA Moscow Russia Pts: 54 GD: -32
  22. Roma Italy Pts: 53 GD: -16
  23. Basel Switzerland Pts: 53 GD: -20
  24. Napoli Italy Pts: 52 GD: 5
  25. AC Milan Italy Pts: 51 GD: -5
  26. Zenit SP Russia Pts: 50 GD: -4
  27. Bayer Leverkusen Germany Pts: 50 GD: -8
  28. Valencia Spain Pts: 48 GD: 16
  29. Sevilla Spain Pts: 48 GD: 6
  30. Ajax Netherlands Pts: 46 GD: -8
  31. Monaco France Pts: 38 GD: -18
  32. Galatasaray Turkey Pts: 35 GD: -30

FIFA World Cup Decade Table: 

fifa-logo-design-history-and-evolution-wkuq7omm-2161994

  1. Germany Pts: 37 GD: 23
  2. Netherlands Pts: 35 GD: 17
  3. Argentina Pts: 32 GD: 5
  4. Brazil Pts: 31 GD: 7
  5. Belgium Pts: 30 GD: 13
  6. France Pts: 30 GD: 12
  7. Uruguay Pts: 29 GD: 5
  8. Spain Pts: 27 GD: 4
  9. Colombia Pts: 19 GD: 11
  10. Croatia Pts: 17 GD: 5
  11. Mexico Pts: 17 GD: -2
  12. England Pts: 16 GD: 0
  13. Switzerland Pts: 15 GD: 0
  14. Portugal Pts: 14 GD: 3
  15. Chile Pts: 13 GD: 0
  16. Japan Pts: 12 GD: -3
  17. Russia Pts: 10 GD: 3
  18. Costa Rica Pts: 10 GD: 0
  19. Sweden Pts: 9 GD: 2
  20. USA Pts: 9 GD: -1
  21. Ghana Pts: 9 GD: -1
  22. Denmark Pts: 9 GD: -2
  23. South Korea Pts: 8 GD: -5
  24. Nigeria Pts: 8 GD: -5
  25. Greece Pts: 8 GD: -5
  26. Ivory Coast Pts: 7 GD: 0
  27. Paraguay Pts: 6 GD: 1
  28. Serbia Pts: 6 GD: -3
  29. Algeria Pts: 5 GD: -2
  30. Italy Pts: 5 GD: -2
  31. Iran Pts: 5 GD: -3
  32. Australia Pts: 5 GD: -12

*The Tiebreakers that are used in the World Cup Group Stages were applied to the table.

2018-2019 NBA Regular Season PCR, EWC, and Aggregate Wins Report

blazers-warriors-2019-hero

Photo Via: rosequarter.com 

With the start of the second round of the NBA Playoffs, I will be showing you my original advanced stats report of the 2018-2019 regular season. Below is a copy of what I wrote for the all star break article, explaining my two original metrics: Pro Con Rating and Estimated Wins Contributed.

In addition, I am including a new metric called Aggregate Wins Contributed, which is the average of the mean and median of different metrics that evaluate the number of wins an NBA Player contributes.

The metrics I aggregate are:

  1. Estimated Wins Contributed (My Original Metric)
  2. Win Shares
  3. Wins Over Replacement (VORP * 2.7)
  4. Hollinger’s Estimated Wins Added
  5. ESPN’s Real Plus Minus Wins
  6. Wins Produced

If you follow my site, you are most likely familiar with my two original NBA Metrics. Pro-Con Rating (PCR) and Estimated Wins Contributed (EWC). Both of these metrics are box score evaluations, and assign linear weightings to a variety of different box score stats.

I have published previous articles regarding the exact methodology and rationale behind the different weights. Therefore I will simply just go over the formulas here without much explanation, which you can find in previous posts. There is one note I would like to add before laying out the formulas and showing you the leaderboards from this season. EWC possesses some inherent adjustments for pace in its formula. This is due to the fact that it divides a player’s total PCR by their team’s total PCR. Therefore the formula does not penalize a player who has lower stats just because his team does not play a type of basketball that increases the number of possessions.

PCR Primer from 2017-2018 Regular Season

EWC Primer from 2017-2018 Season

PCR Formula: Pro Con Rating, a metric that evaluates how well players are playing statistically on a per game basis. Assigning positive weights to pros (Good Box score stats) and negative weights to cons (Bad box score stats). 
((Pts)+(Reb)+(Ast x 1.4)+(Blks x 1.2)+(Steals x 1.1))-((TOs)+(Field Goals Missed)+(Free Throws Missed x .5)+(Fouls))/Games Played

Total PCR: Total Pro Con Rating, just the total PCR not on a per game basis. 

PCR * Games Played

PCR/36: Pro Con Rating Per 36 Minutes, a way to evaluate how efficient players are given the pro-con rating metric. 

((Total PCR)/(Minutes Played))*36

EWC Formula:  Estimated Wins Contributed based on box score statistics. 

(Player’s Total PCR/Team PCR)*Wins

EWC/82 Formula: Estimated Wins Contributed Per 82 Games, how many wins the player would have if they played 82 games based on their current rate and statistics. 

(EWC/Games Played)*82

EWC/48 Formula: Estimated Wins Contributed Per 48 Minutes, an efficiency metric evaluating how many wins per 48 minutes a specific player is estimated to contribute. 

(EWC/Minutes Played)*48

Updated General Guidelines for Ratings:

PCR>32: MVP Candidate

PCR>30: Top Tier Player in the League

PCR>27: 1st Team/2nd Team All NBA Talent

PCR>25: For Sure All Star Possible Third Team All NBA

PCR>22: Borderline All Star

PCR>20: Good But Not a Great Player

PCR>17: Good Solidified Starter

PCR>13: Starter and Good Team Contributor

PCR>10: Valuable Role Player

PCR>7.5: Average Player

PCR<5.0: Below Average

PCR Top 25: 

*Qualifier = Total PCR > 1000

  1. Giannis Antetokounmpo MIL: 36.11
  2. Anthony Davis NOP: 33.99
  3. James Harden HOU: 33.98
  4. LeBron James LAL: 33.21
  5. Joel Embid PHI: 31.83
  6. Russell Westbrook OKC: 31.30
  7. Nikola Jokic DEN: 29.60
  8. Kevin Durant GSW: 28.94
  9. Karl-Anthony Towns MIN: 28.81
  10. Nikola Vucevic ORL: 28.22
  11. Kawhi Leonard TOR: 27.52
  12. Paul George OKC: 27.15
  13. Rudy Gobert UTA: 26.61
  14. Ben Simmons PHI: 26.53
  15. Damian Lillard POR: 26.41
  16. Stephen Curry GSW: 26.21
  17. Kyrie Irving BOS: 26.02
  18. Andre Drummond DET: 25.97
  19. Clint Capela HOU: 25.37
  20. Jrue Holiday NOP: 24.81
  21. Bradley Beal WAS: 24.41
  22. Blake Griffin DET: 24.23
  23. Kemba Walker CHA: 23.95
  24. LaMarcus Aldridge SAS: 23.81
  25. DeMar DeRozan SAS: 23.43

Minute Adjusted PCR (PCR/36) Top 25: 

*Qualifier = Total PCR > 1000

  1. Giannis Antetokounmpo MIL: 39.69
  2. Anthony Davis NOP: 37.04
  3. Nikola Jokic DEN: 34.05
  4. Joel Embid PHI: 34.05
  5. LeBron James LAL: 33.94
  6. James Harden HOU: 33.29
  7. Nikola Vucevic ORL: 32.38
  8. Karl-Anthony Towns MIN: 31.34
  9. Russell Westbrook OKC: 31.28
  10. Rudy Gobert UTA: 30.11
  11. Kevin Durant GSW: 30.08
  12. Kawhi Leonard TOR: 29.14
  13. Hassan Whiteside MIA: 28.99
  14. Kyrie Irving BOS: 28.34
  15. Jusuf Nurkic POR: 28.00
  16. Ben Simmons PHI: 27.95
  17. Stephen Curry GSW: 27.93
  18. Andre Drummond DET: 27.90
  19. Domantas Sabonis IND: 27.25
  20. Clint Capela HOU: 27.21
  21. Damian Lillard POR: 26.80
  22. Paul George OKC: 26.49
  23. DeAndre Jordan NYK: 26.26
  24. Enes Kanter POR: 26.10
  25. Luka Doncic DAL: 26.95

Total PCR Top 25: 

  1. James Harden HOU: 2650.8
  2. Giannis Antetokounmpo MIL: 2599.8
  3. Nikola Jokic DEN: 2368.3
  4. Russell Westbrook OKC: 2284.9
  5. Nikola Vucevic ORL: 2257.7
  6. Kevin Durant GSW: 2257.4
  7. Karl-Anthony Towns MIN: 2218.3
  8. Rudy Gobert UTA: 2155.4
  9. Damian Lillard POR: 2122.5
  10. Ben Simmons PHI: 2095.9
  11. Paul George OKC: 2090.5
  12. Andre Drummond DET: 2051.5
  13. Joel Embid PHI: 2037.1
  14. Bradley Beal WAS: 2001.9
  15. Kemba Walker CHO: 1963.6
  16. LaMarcus Aldridge SAS: 1928.8
  17. Anthony Davis NOP: 1903.5
  18. LeBron James LAL: 1826.4
  19. Blake Griffin DET: 1817.6
  20. Stephen Curry GSW: 1808.6
  21. DeMar DeRozan SAS: 1804.3
  22. Kyrie Irving BOS: 1743.2
  23. Tobias Harris PHI: 1711.6
  24. Clint Capela HOU: 1699.7
  25. D’Angelo Russell BRK: 1674.7

Estimated Wins Contributed Top 25: 

  1. James Harden HOU: 14.90
  2. Giannis Antetokounmpo MIL: 14.01
  3. Nikola Jokic DEN: 12.47
  4. Kevin Durant GSW: 11.40
  5. Russell Westbrook OKC: 11.33
  6. Damian Lillard POR: 11.05
  7. Rudy Gobert UTA: 10.52
  8. Paul George OKC: 10.37
  9. Ben Simmons PHI: 10.10
  10. Joel Embid PHI: 9.82
  11. Nikola Vucevic ORL: 9.66
  12. Clint Capela HOU: 9.55
  13. Andre Drummond DET: 9.52
  14. Kawhi Leonard TOR: 9.22
  15. Stephen Curry GSW: 9.13
  16. LaMarcus Aldridge SAS: 9.04
  17. Pascal Siakam TOR: 8.60
  18. DeMar DeRozan SAS: 8.46
  19. Blake Griffin DET: 8.44
  20. Tobias Harris PHI: 8.30
  21. Kyrie Irving BOS: 8.27
  22. Eric Bledsoe MIL: 8.17
  23. Karl-Anthony Towns MIN: 8.03
  24. Jusuf Nurkic POR: 8.03
  25. Kemba Walker CHO: 7.92

EWC/82 Top 25: 

*Qualifier= Total PCR > 1000

  1. Giannis Antetokounmpo MIL: 15.95
  2. James Harden HOU: 15.66
  3. Nikola Jokic DEN: 12.78
  4. Russell Westbrook OKC: 12.73
  5. Kawhi Leonard TOR: 12.60
  6. Joel Embid PHI: 12.58
  7. Kevin Durant GSW: 11.98
  8. Clint Capela HOU: 11.69
  9. Damian Lillard POR: 11.32
  10. Paul George OKC: 11.04
  11. Stephen Curry GSW: 10.85
  12. Rudy Gobert UTA: 10.65
  13. Ben Simmons PHI: 10.48
  14. LeBron James LAL: 10.16
  15. Kyrie Irving BOS: 10.12
  16. Chris Paul HOU: 9.93
  17. Nikola Vucevic ORL: 9.90
  18. Andre Drummond DET: 9.88
  19. Kyle Lowry TOR: 9.62
  20. Blake Griffin DET: 9.22
  21. LaMarcus Aldridge SAS: 9.15
  22. Jusuf Nurkic POR: 9.14
  23. DeMar DeRozan SAS: 9.01
  24. Pascal Siakam TOR: 8.81
  25. Anthony Davis NOP: 8.77

Minute Adjusted EWC (EWC/48) Top 25: 

*Qualifier= Total PCR > 1000

  1. Giannis Antetokounmpo MIL: .285
  2. James Harden HOU: .249
  3. Nikola Jokic DEN: .239
  4. Joel Embid PHI: .219
  5. Kawhi Leonard TOR: .217
  6. Russel Westbrook OKC: .207
  7. Clint Capela HOU: .204
  8. Kevin Durant GSW: .202
  9. Rudy Gobert UTA: .196
  10. Jusuf Nurkic POR: .195
  11. Stephen Curry GSW: .188
  12. Damian Lillard POR: .187
  13. Nikola Vucevic ORL: .185
  14. Chris Paul HOU: .182
  15. Ben Simmons PHI: .180
  16. Kyrie Irving BOS: .179
  17. Paul George OKC: .175
  18. Domantas Sabonis IND: .175
  19. Andre Drummond DET: .173
  20. Eric Bledsoe MIL: .173
  21. LeBron James LAL: .169
  22. Serge Ibaka TOR: .166
  23. Lou Williams LAC: .166
  24. Montrezl Harrell LAC: .166
  25. Kyle Lowry TOR: .165

Aggregate Wins Contributed:

  1. James Harden HOU: 19.01 Max: 28.00 Min: 14.90
  2. Giannis Antetokounmpo MIL: 17.42 Max: 22.80 Min: 14.01
  3. Rudy Gobert UTA: 15.48 Max: 22.90 Min: 10.52
  4. Nikola Jokic DEN: 15.23 Max: 19.71 Min: 11.80
  5. Paul George OKC: 14.64 Max: 19.73 Min: 10.37
  6. Damian Lillard POR: 14.18 Max: 18.00 Min: 11.05
  7. Nikola Vucevic ORL: 13.42 Max: 18.60 Min: 9.66
  8. Karl-Anthony Towns MIN: 12.92 Max: 20.00 Min: 8.03
  9. Kevin Durant GSW: 12.90 Max: 18.50 Min: 11.40
  10. Stephen Curry GSW: 12.67 Max: 15.60 Min: 9.13
  11. Anthony Davis NOP: 11.90 Max: 17.30 Min: 5.99
  12. Russell Westbrook OKC: 11.83 Max: 15.12 Min: 6.80
  13. Kyrie Irving BOS: 11.12 Max: 14.70 Min: 8.27
  14. Ben Simmons PHI: 10.99 Max: 18.20 Min: 8.20
  15. Andre Drummond DET: 10.84 Max: 17.30 Min: 8.79
  16. Clint Capela HOU: 10.62 Max: 16.60 Min: 7.02
  17. LeBron James LAL: 10.42 Max: 14.60 Min: 6.81
  18. Joel Embid PHI: 10.22 Max: 16.70 Min: 8.70
  19. Kawhi Leonard TOR: 10.21 Max: 15.60 Min: 8.21
  20. Kemba Walker CHA: 9.84 Max: 15.30 Min: 7.40
  21. Bradley Beal WAS: 9.58 Max: 15.60 Min: 6.39
  22. Steven Adams OKC: 9.28 Max: 13.50 Min: 7.55
  23. Eric Bledsoe MIL: 9.28 Max: 12.60 Min: 8.17
  24. Jusuf Nurkic POR: 9.27 Max: 12.60 Min: 7.80
  25. Pascal Siakam TOR: 9.25 Max: 11.91 Min: 7.10

Lastly, here is the spreadsheet with every player’s PCR and EWC. Additionally, there is a leaderboard of 48 players and their aggregate wins contributed. These 48 players were chosen because they were all ranked in the Top 25 in at least one of the 6 Wins Metrics. The Players of interest sheet included players with good stats and other players I thought were interesting.

 

March Madness 2019 Predictions and Hybrid Rating Index

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Photo Via:  Duke Chronicle

The madness is upon us! Will Zion and Duke’s fabulous freshman cut down the nets? Or will Luke Maye and UNC a hybrid team full of both veterans and young stars ruin the Blue Devils Party? Can UVA bounce back? All of these questions and headlines will be answered in the tournament.

March Madness is nearly impossible to predict, hence the name “Madness”. Last Year may have been the greatest indicator of that #16 UMBC beat the #1 overall seed UVA by 20 for the first time in the tournament’s history, #13 Buffalo smacked #4 Arizona, #11 Loyola rolled past #9 K-State in the Elite 8 to make the Final 4, #7 Nevada completed a sensational comeback over #2 Cincinnati, #13 Marshall shocked the Shockers of Wichita State, there were various over shocking results from last year’s tournament.

With that being said, this NCAA Tournament Rating Index includes various subjective and objective factors. The 2 Main Components plus an adjustment for W-L% in Last 12 Games)

  1. A Talent Index
  2. My Regular Season Hybrid NCAAB Computer Ratings (Retrodictive and Predictive)

1: Talent Index:

(Weighted Avg 247 Composite Rating+* (Weighted Avg of 247 Star Rating*)+(Average Borda Count of Preseason Rankings* x .5))/3)+(Weighted Avg of Individual PERs* x 1.35)) x 2)

Weighted Avg 247 Comp. Rating= Σ (Player’s Minutes Played/(Σ of Every Player’s Minutes))*Player’s Composite Rating

Weighted Avg of 247 Star Rating= Σ (Player’s Minutes Played/(Σ of Every Player’s Minutes)) *(247 Composite Star Rating/5)

Borda Count of Preseason Rankings: ((Coaches Poll Pts Received)/(25* Number of Voters in Poll))+((AP Poll Pts Received)/(25*Number of Voters in Poll)))/2)*.5

Weighted Avg of Individual PERs: Σ (Player’s Minutes Played/(Σ of Every Player’s Minutes))*(Player’s Player Efficiency Rating))*1.35

This was indeed the subjective facet of the Index. You may be wondering why to include this at all? There have been various analyses that show talented teams often get their act together in the NCAA Tournament if they have underachieved in the regular season. There is no better example of this than 2014 Kentucky, who was preseason #1, and was chock full of Five Star Talent, but finished outside of the Top 25 and was an 8 seed going into the tournament. They then advanced through what may have been the hardest region, all the way to the National Title Game.

To measure a “Team’s Talent” is a purely subjective matter, however, there were analytics and statistics available that could at least encapsulate much of a team’s talent. The first two I used were recruiting data regarding each team’s roster. I used 247sports.com and cross-reference the players’ individual composite ratings and star ratings to the minutes’ data on https://www.sports-reference.com/cbb/.  To weight player’s who played more minutes heavily. For players who did not garner a star rating or composite rating, they were assigned a .5 for their composite rating and a 0-star rating. Adjusting the recruiting ratings to the minutes played instead of using a plain ole average for the composite and star ratings was important, because otherwise, the players who were highly touted out of high school but turned out to be complete busts would increase a team’s rating much more. Through adjusting that player’s composite rating’s impact on the overall talent rating has greatly decreased. This also adjusts for the team’s who have had their 5-star players injured throughout the year like Oregon’s Bol Bol.

The third method I used to rate a team’s talent was the Average Borda Count of a Team’s Preseason Rankings in the AP Poll and Coaches Poll. At first, you may be wondering why I would use this. The reasoning mostly is based on the fact that a 30ish/35ish game regular season is not a very sufficient sample size. As FiveThirtyEight said in their employment of preseason rankings for their March Madness Predictions, “Preseason rankings provide some estimate of each team’s underlying player and coaching talent. It’s a subjective estimate, but it nevertheless adds some value, based on our research.” Again, 2014 preseason #1 Kentucky is a great example of why this can be of great assistance. To calculate these I used a Borda Count which is simply the amount of points received divided by the max points received, and in the polls the max points is recieved is just the # of Voters multiplied by 25. I also included teams who received votes but did not make the Top 25, in the Borda Counts. Teams who did not receive any preseason votes were assigned a 0, and to adjust for greater variation in this metric I multiplied it by .5 to level the standard deviations between this facet and the others.

The last method I used for approximating a team’s talent was Player Efficiency Rating. PER is a lengthy and complicated box score efficiency metric made by John Hollinger. Being an efficiency metric means that it is roughly based on a per minute basis. To adjust for this I used the same method I used for the Recruiting Ratings, and multiplied it by the % of Minutes Played, then summed it up. I used PER because this allows us to gain insight into players who may not have been the most highly recruited but have had great regular season performances. Some notable examples of this are the unrated Ja Morant of Murray State, 3* Brandon Clarke of Gonzaga, 3* Jarett Culver of Texas Tech, and 3* Ethan Happ of Wisconsin.

The calculations behind combining these different facets are mentioned above.

Part 2: Predictive and Retrodictive Hybrid Rating 

Team Computer Rating: (W-L%)+(Strength of Schedule* x 1.5)+((.01 xAdj Net Rating*)+(.01 x Adjusted Scoring Margin* x 1.75)) x 1.35))/3

Strength of Schedule: (2/3 * Opponents W-L%)+(1/3 * Opponent’s Opponents’ W-L%)

Adjusted Net Rating: 100*(Pts-Opp Pts)/Poss), Then it is adjusted for the opponent’s average Adjusted Net Rating in an iterative processs.

Adjusted Scoring Margin: (Pts-Opp Pts)/(Games Played), Then it is adjusted for the opponent’s average Adjusted Net Rating in an iterative process.

Via: Sports Reference

The second facet of my index was a rating system for teams that combined retrodictive aspects and predictive aspects into one. It is quite similar to the ratings I have done for other sports, such as my NBA Metric. The Adjusted Net Ratings and Adjusted Scoring Margins were calculated by https://www.sports-reference.com/cbb/.

One thing you may notice is that I assign more importance to the scoring margin and net rating metrics than I normally do, this is because I made this index with the intent of predicting March Madness Games, and scoring margin metrics tend to be more predictive in nature.

Lastly, I added an adjustment for W-L% in Last 12 Games.

Therefore the Final Formula was:

((W-L% in Last 12 x .01)+(Talent Rating x .3)+(Computer Rating x .7)

The Ratings:

 

Here is a bracket using the metric:

Screenshot 2019-03-21 10.53.16.png

NBA All-Star Break Advanced Stats Leaderboard (PCR and Estimated Wins Contributed)

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Photo Via:  Houston Chronicle

 

If you follow my site, you are most likely familiar with my two original NBA Metrics. Pro-Con Rating (PCR) and Estimated Wins Contributed (EWC). Both of these metrics are box score evaluations, and assign linear weightings to a variety of different box score stats.

I have published previous articles regarding the exact methodology and rationale behind the different weights. Therefore I will simply just go over the formulas here without much explanation, which you can find in previous posts.

PCR Formula: Pro Con Rating, a metric that evaluates how well players are playing statistically on a per game basis. Assigning positive weights to pros (Good Box score stats) and negative weights to cons (Bad box score stats). 
((Pts)+(Reb)+(Ast x 1.4)+(Blks x 1.2)+(Steals x 1.1))-((TOs)+(Field Goals Missed)+(Free Throws Missed x .5)+(Fouls))/Games Played

Total PCR: Total Pro Con Rating, just the total PCR not on a per game basis. 

PCR*Games Played

PCR/36: Pro Con Rating Per 36 Minutes, a way to evaluate how efficient players are given the pro-con rating metric. 

((Total PCR)/(Minutes Played))*36

EWC Formula:  Estimated Wins Contributed based on box score statistics. 

(Player’s Total PCR/Team PCR)*Wins

EWC/82 Formula: Estimated Wins Contributed Per 82 Games, how many wins the player would have if they played 82 games based on their current rate and statistics. 

(EWC/Games Played)*82

EWC/48 Formula: Estimated Wins Contributed Per 48 Minutes, an efficiency metric evaluating how many wins per 48 minutes a specific player is estimated to contribute. 

(EWC/Minutes Played)*48

 

PCR Leaderboard:

Updated General Guidelines for Ratings:

PCR>32: MVP Candidate

PCR>30: Top Tier Player in the League

PCR>27: 1st Team/2nd Team All NBA Talent

PCR>25: For Sure All Star Possible Third Team All NBA

PCR>22: Borderline All Star

PCR>20: Good But Not Great Player

PCR>17: Good Solidified Starter

PCR>13: Starter and Good Team Contributor

PCR>10: Valuable Role Player

PCR>7.5: Average Player

PCR<5.0: Below Average

 

Qualifyer: Total PCR >/= 700

  1. Anthony Davis NOP: 36.64
  2. Giannis Anteokounmpo MIL: 35.75
  3. James Harden HOU: 34.38
  4. LeBron James LAL: 32.69
  5. Joel Embid PHI: 31.09
  6. Russell Westbrook OKC: 30.94
  7. Kevin Durant GSW: 30.47
  8. Nikola Jokic DEN: 30.26
  9. Nikola Vucevic ORL: 28.16
  10. Kawhi Leonard TOR: 28.12
  11. Paul George OKC: 28.09
  12. Karl-Anthony Towns MIN: 27.84
  13. Stephen Curry GSW: 27.30
  14. Ben Simmons PHI: 26.88
  15. Rudy Gobert UTA: 26.39
  16. Kyrie Irving BOS: 26.18
  17. Damian Lillard POR: 26.02
  18. Blake Griffin DET: 25.99
  19. Clint Capela HOU: 25.72
  20. Jrue Holiday NOP: 25.05
  21. Andre Drummond DET: 24.55
  22. Bradley Beal WAS: 23.52
  23. Kemba Walker CHA: 23.22
  24. LaMarcus Aldridge POR: 23.16
  25. John Wall WAS: 22.90

Total PCR Leaderboard:

  1. Giannis Anteokounmpo MIL Total PCR: 1894.5
  2. James Harden HOU Total PCR: 1856.7
  3. Kevin Durant GSW Total PCR: 1736.8
  4. Nikola Jokic DEN Total PCR: 1694.8
  5. Joel Embid PHI Total PCR: 1678.9
  6. Anthony Davis NOP Total PCR: 1648.7
  7. Nikola Vucevic ORL Total PCR: 1633.4
  8. Karl-Anthony Towns MIN Total PCR: 1586.6
  9. Paul George OKC Total PCR: 1572.8
  10. Ben Simmons PHI Total PCR: 1531.9
  11. Russell Westbrook OKC Total PCR: 1516.2
  12. Rudy Gobert UTA Total PCR: 1504.4
  13. Jrue Holiday NOP Total PCR: 1477.7
  14. Damian Lillard POR Total PCR: 1457.0
  15. Blake Griffin DET Total PCR: 1403.6
  16. Bradley Beal WAS Total PCR: 1363.9
  17. LaMarcus Aldridge SAS Total PCR: 1343.2
  18. Kemba Walker CHA Total PCR: 1323.6
  19. Andre Drummond DET Total PCR: 1301.1
  20. LeBron James LAL Total PCR: 1274.9
  21. Tobias Harris LAC/PHI Total PCR: 1270.4
  22. Stephen Curry GSW Total PCR: 1255.7
  23. DeAndre Jordan DAL/NYK Total PCR: 1234.8
  24. Kyrie Irving BOS Total PCR: 1230.4
  25. DeMar DeRozan SAS Total PCR: 1228.5

PCR/36:

Total PCR >/= 700.0

  1. Giannis Antetokounmpo MIL: 38.82
  2. Anthony Davis NOP: 36.68
  3. Nikola Jokic DEN: 34.53
  4. LeBron James LAL: 33.67
  5. Joel Embid PHI: 33.17
  6. James Harden HOU: 33.11
  7. Nikola Vucevic ORL: 32.63
  8. Russell Westbrook OKC: 31.30
  9. Kevin Durant GSW: 31.00
  10. Karl-Anthony Towns MIN: 30.25
  11. Rudy Gobert UTA: 30.09
  12. Kawhi Leonard TOR: 29.12
  13. Kyrie Irving BOS: 29.03
  14. Stephen Curry GSW: 28.68
  15. Ben Simmons PHI: 28.60
  16. Hassan Whiteside MIA: 28.00
  17. Paul George OKC: 27.84
  18. Domantas Sabonis IND: 27.51
  19. Jusuf Nurkic POR: 27.25
  20. Clint Capela HOU: 27.08
  21. Andre Drummond DET: 26.80
  22. Julius Randle NOP: 26.58
  23. Damian Lillard POR: 26.53
  24. Enes Kanter NYK/POR: 26.13
  25. Lou Williams LAC: 25.92

EWC:

  1. Giannis Antetokounmpo MIL: 10.64
  2. James Harden HOU: 9.59
  3. Nikola Jokic DEN: 9.11
  4. Kevin Durant GSW: 8.98
  5. Paul George OKC: 8.27
  6. Joel Embid PHI: 8.25
  7. Russell Westbrook OKC: 7.97
  8. Ben Simmons PHI: 7.53
  9. Damian Lillard POR: 7.16
  10. Kawhi Leonard TOR: 7.07
  11. Rudy Gobert UTA: 7.07
  12. Stephen Curry GSW: 6.49
  13. Nikola Vucevic ORL: 6.46
  14. Pascal Siakam TOR: 6.34
  15. Karl-Anthony Towns MIN: 6.21
  16. Blake Griffin DET: 6.19
  17. Kyrie Irving BOS: 6.15
  18. Eric Bledsoe MIL: 6.05
  19. LaMarcus Aldridge SAS: 6.05
  20. Kyle Lowry TOR: 5.88
  21. Tobias Harris LAC/PHI: 5.85
  22. Steven Adams OKC: 5.80
  23. Andre Drummond DET:5.74
  24. Jusuf Nurkic POR: 5.69
  25. Domantas Sabonis IND: 5.66

EWC/82:

Total PCR >/=700

  1. Giannis Antetokounmpo MI: 16.47
  2. James Harden HOU: 14.57
  3. Kawhi Leonard TOR: 13.48
  4. Russell Westbrook OKC: 13.35
  5. Nikola Jokic DEN: 13.34
  6. Kevin Durant GSW: 12.91
  7. Joel Embid PHI: 12.53
  8. Paul George OKC: 12.11
  9. Stephen Curry GSW: 11.57
  10. Clint Capela HOU: 10.90
  11. LeBron James LAL: 10.88
  12. Ben Simmons PHI: 10.83
  13. Kyrie Irving BOS: 10.73
  14. Damian Lillard POR: 10.49
  15. Anthony Davis NOP: 10.27
  16. Kyle Lowry TOR: 10.25
  17. Rudy Gobert UTA: 10.17
  18. Blake Griffin DET: 9.40
  19. Nikola Vucevic ORL: 9.13
  20. Pascal Siakam TOR: 8.96
  21. Karl-Anthony Towns MIN: 8.93
  22. Andre Drummond DET: 8.88
  23. Eric Bledsoe MIL: 8.85
  24. Chris Paul HOU: 8.81
  25. Steven Adams OKC: 8.65

EWC/48:
Total PCR >/=700

  1. Giannis Antetokounmpo MIL: .291
  2. Nikola Jokic DEN: .248
  3. James Harden HOU: .228
  4. Kawhi Leonard TOR: .227
  5. Russell Westbrook OKC: .219
  6. Joel Embid PHI: .217
  7. Kevin Durant GSW .214
  8. Stephen Curry GSW:. 198
  9. Domantas Sabonis IND: .196
  10. Paul George OKC: .195
  11. Kyrie Irving BOS: .193
  12. Rudy Gobert UTA: .188
  13. Ben Simmons PHI: .187
  14. Clint Capela HOU: .187
  15. LeBron James LAL: .182
  16. Jusuf Nurkic POR: .179
  17. Eric Bledsoe MIL: .178
  18. Kyle Lowry TOR: .175
  19. Damian Lillard POR: .174
  20. Nikola Vucevic ORL: .172
  21. Serge Ibaka TOR: .167
  22. Anthony Davis NOP: .167
  23. Pascal Siakam: .166
  24. Al Horford BOS: .163
  25. Lou Williams LAC: .159

 

PCR Top 5 by Position:

Key:

(#)=PCR/EWC/82

:#/#/#/#/#/%= PPG/RPG/APG/SPG/BPG/FG%

Point Guard:

  1. Russell Westbrook OKC (30.94): 22.1/11.3/11.1/2.2/0.4/42.0%
  2. Stephen Curry GSW (27.30): 28.8/5.1/5.3/1.2/0.4/48.9%
  3. Ben Simmons PHI (26.88): 16.8/9.0/7.8/1.3/0.7/56.8%
  4. Kyrie Irving BOS (26.18): 23.5/4.8/6.9/1.6/0.5/49.3%
  5. Damian Lillard POR (26.02): 26.1/4.5/6.4/1.2/0.5/44.6%

Shooting Guard:

  1. James Haden HOU (34.38): 36.5/6.7/7.7/2.1/0.8/44.2%
  2. Bradley Beal WAS (23.52): 25.5/5.1/5.4/1.4/0.8/47.5%
  3. DeMar DeRozan SAS (22.75): 21.4/6.1/6.2/1.0/0.5/46.4%
  4. Jimmy Butler PHI/MIN (22.14): 19.3/5.1/4.0/2.0/0.6/47.9%
  5. Devin Booker PHO (21.28): 24.7/3.9/6.7/0.9/0.2/46.1%

Small Forward:

  1. LeBron James LAL (32.69): 26.8/8.7/7.6/1.3/0.7/51.2%
  2. Kevin Durant GSW (30.47): 27.6/7.0/5.9/0.8/1.2/51.6%
  3. Kawhi Leonard TOR (28.12): 26.9/7.6/3.3/1.9/0.5/48.8%
  4. Paul George OKC (28.09): 28.9/8.0/4.2/2.3/0.5/45.5%
  5. Luka Doncic DAL (22.13): 20.7/7.2/5.6/1.0/0.3/43.0%

Power Forward:

  1. Anthony Davis NOP (36.64): 27.8/12.8/4.2/1.6/2.5/50.8%
  2. Giannis Antetokounmpo MIL (35.75): 27.2/12.7/6.0/1.4/1.4/58.1%
  3. Blake Griffin DET (25.99): 26.1/8.0/5.3/0.7/0.4/47.9%
  4. John Collins ATL (20.74):  19.1/9.4/2.0/0.3/0.4/56.8%
  5. Domantas Sabonis IND (18.92): 14.1/9.3/2.8/0.7/0.4/60.9%

Center:

  1. Joel Embid PHI (31.09): 27.3/13.5/3.5/0.6/1.9/48.2%
  2. Nikola Jokic DEN (30.26): 20.4/10.7/7.7/1.4/0.6/50.3%
  3. Nikola Vucevic ORL (28.16): 20.5/12.1/3.9/0.9/1.2/52.2%
  4. Karl-Anthony Towns MIN (27.84): 23.1/12.0/3.2/0.9/1.851.2%
  5. Rudy Gobert UTA (26.39): 15.4/12.9/2.2/0.9/2.2/65.7%

EWC/82 Top 5 by Position:

Point Guard

  1. Russell Westbrook OKC (13.35): 22.1/11.3/11.1/2.2/0.4/42.0%
  2. Stephen Curry GSW (11.57): 28.8/5.1/5.3/1.2/0.4/48.9%
  3. Ben Simmons PHI (10.83): 16.8/9.0/7.8/1.3/0.7/56.8%
  4. Kyrie Irving BOS (10.73): 23.5/4.8/6.9/1.6/0.5/49.3%
  5. Damian Lillard POR (10.49): 26.1/4.5/6.4/1.2/0.5/44.6%

Shooting Guard

  1. James Harden HOU (14.57): 36.5/6.7/7.7/2.1/0.8/44.2%
  2. Victor Oladipo IND (8.58): 18.8/5.6/5.2/1.7/0.3/42.3%
  3. Jimmy Butler PHI/MIN (8.49): 19.3/5.1/4.0/2.0/0.6/47.9%
  4. DeMar DeRozan SAS (8.40): 21.4/6.1/6.2/1.0/0.5/46.4%
  5. Malcolm Brogdon MIL (7.90):  15.7/4.8/3.3/0.7/0.2/50.3%

Small Forward:

  1. Kawhi Leonard TOR (13.48): 26.9/7.6/3.3/1.9/0.5/48.8%
  2. Kevin Durant GSW (12.91): 27.6/7.0/5.9/0.8/1.2/51.6%
  3. Paul George OKC (12.11): 28.9/8.0/4.2/2.3/0.5/45.5%
  4. LeBron James LAL (10.88): 26.8/8.7/7.6/1.3/0.7/51.2%
  5. Tobias Harris LAC/PHI (8.13): 20.8/7.9/2.7/0.7/0.5/49.9%

Power Forward:

  1. Giannis Antetokounmpo MIL (16.47): 27.2/12.7/6.0/1.4/1.5/58.1%
  2. Anthony Davis NOP (10.25): 27.8/12.8/4.2/1.6/2.5/50.8%
  3. Blake Griffin DET (9.40): 26.1/8.0/5.3/0.7/0.4/47.9%
  4. Pascal Siakam TOR (8.96): 16.2/6.9/2.9/1.0/0.7/55.4%
  5. Domantas Sabonis IND (8.29): 14.1/9.3/2.8/0.7/0.4/60.9%

Center:

  1. Nikola Jokic DEN (13.34): 20.4/10.7/7.7/1.4/0.6/50.3%
  2. Joel Embid PHI (12.53): 27.3/13.5/3.5/0.6/1.9/48.2%
  3. Clint Capela HOU (10.90): 17.3/12.6/1.4/0.6/1.8/63.0%
  4. Rudy Gobert UTA (10.17): 15.4/12.9/2.2/0.9/2.2/65.7%
  5. Nikola Vucevic (ORL 9.13):  20.5/12.1/3.9/0.9/1.2/52.2%

 

Here is the spreadsheet with the total stats of the players as well as every player’s PCR and EWC stats. The individual’s basic stats (Not PCR and EWC), were pulled from https://www.basketball-reference.com/.