Brady, Smith Generate Highest Percentage Of Wins For Their NFL Teams
BY ADAM GROSSMAN & ROSS CHUMSKY
Update – The trade reported yesterday of Kansas City Chiefs QB Alex Smith to the Washington Redskins is a good example of how to use our new winning metric. As part of this deal, it is likely that the Redskins will not re-sign QB Kirk Cousins, trade cornerback Kendall Fuller to the Chiefs, and trade a third round draft pick in the 2018 draft to the Chiefs. The Redskins will receive a 2019 third round compensatory pick when another team signs Cousins. Our analysis of the 2017 season shows that Smith generated a 1.67 wins while Cousins and Fuller combined generated 1.14 wins. This demonstrates that Redskins will receive more wins from these transactions if Alex Smith performs at or near his 2017 performance while Cousins remains at or near his 2017 performance. For the Chiefs, providing the opportunity for Patrick Mahomes to become its starting QB at a lower salary may mean fewer wins in the short-term but at a lower cost than keeping Smith
What NFL player has the most impact on his team? It is common question, and unlike baseball and basketball, football does not have a consensus, baseline advanced analytic metric to rate players. More specifically, Wins Above Replacement (WAR) is typically used in baseball and Player Efficiency Rating (PER) is typically used in basketball. The idea behind both metrics is to quantify an individual player’s impact on winning above a minimal level of performance. With WAR, for example, a player’s overall contribution is essentially based on how many runs he created and how many runs he prevented as compared to a Triple-A player in the same position.
The reason that it has been more difficult to create this type of metric for football is that the sport relies on the performance of multiple team members on every play. This complexity makes it difficult to identify an individual player’s contribution. For example, the typical successful passing play requires an offensive line to protect a quarterback from a sack, a quarterback to throw the ball, and a wide receiver to catch the pass. Who deserves the most credit if the play is executed successfully?
In the process of creating the Revenue Above Replacement (RAR) for the NFL for this season, our team at Block Six Analytics (B6A) identified the need to create a version of this on-field metric. RAR examines how an individual player generates revenue for a team based on his on field, off field, and personal performance. In the past, we relied on WAR or PER to create our models for the MLB and NBA, respectively.
To solve this problem for football, we used multiple data sets and multi-factor regression analysis. We were able to determine two main attributes that accounted for wins. In essence, these factors are how well (Grade) and how much (Snaps) did a player perform during the 2017 season.
We found that these two factors identified how many yards over a replacement level player each player contributed to his team (the equivalent of how many runs were created in baseball). We found that summing each individual player’s yards contribution for a team in conjunction with regressing a team’s total wins provided a strong and statistically significant descriptive “winning” statistic.
We were also able to attribute yards to every single football player in the NFL this season. The top-15 players in our analysis are in Table Two. The first area of focus is PlayerWin%. This describes how much that player contributes to a team’s win over a replacement level player (in football think an undrafted free agent) with a 100% meaning the player accounts for all of a team’s wins. Unsurprisingly, Tom Brady leads the NFL in PlayerWin% with the Patriots quarterback accounting for 18.04% of team’s wins. What else is likely not surprising is that quarterbacks makeup all of the top-10 and 11 of the top-15 players in this rating.
What is likely more surprising is that Kansas City Chiefs quarterback Alex Smith had the second highest PlayerWin%. How valuable is he? Let’s examine the fbWAR, ExpectedWin% and WinDelta columns. fbWAR calculates the wins a player adds over a sixteen game schedule (fb stands for football in our metric). ExpectedWin% looks at what percent of a team’s wins a player should contribute on an average team. WinDelta looks as the differences between the PlayerWin% and the ExpectedWin%. A positive number means the player is more valuable to that team than to the average team. A negative value means a player is less valuable to the team than the average team.
Not only did Smith have a Pro Bowl year, he is particularly valuable to the Chiefs because he had the highest WinDelta of any player in the analysis. His PlayerWin% is so high because the Chiefs as a whole have fewer players that contribute wins above a replacement level player. In our model, the Chiefs were expected to win 7 games (as seen in Table One) given the performance of the players on its roster (meaning the team won more game that would be expected given the talent on its roster). As a contrast, Brady has a negative WinDelta meaning he would be even more valuable on a different team than he was to the Patriots.
The three Panthers players followed the same pattern. These players are more valuable to the Panthers than to other teams because the Panthers had an even lower win contribution from its players than the Chiefs. The Panthers were also expected to win 7 games given the performance of its players (and actually won 11).
What if you wanted to see a player’s contribution overall (not measured to a specific team)? Table Three sorts players by fbWar instead of PlayerWin%. Once again, Brady leads the way in this metric meaning he generated the most wins and the highest contribution of his teams win total. However, Smith drops to 9th in this analysis. Two other players of note are Jimmy Garopplo and Alvin Kamara. Garoppolo ranked 13th overall even though he only played in five games. Had he performed in this same way over a 16 game season then he would have surpassed Brady on this list. Kamara also had relatively few plays this year as compared to other skilled position players (running backs, wide receivers, and tight ends) yet still finished as the highest non-QB on this list.
Why have we not called Brady the Most Valuable Player? We mentioned that we originally completed this analysis as part of our RAR model. Our approach looks at players’ contributions on and off field. Our next blog post will have the completed RAR analysis where we can determine the overall value of each player. In terms of wins generated, however, Brady is the highest performing player.