In Depth Statistical Look at MVP, Money, Player Possession, Most Valuable Position, and more for 2016-17 season
The 2016-17 NBA regular season has been one of record setting and exceptional performances. People have been raving about who should get the MVP. While I heard many different opinions, I wanted to come to my own conclusion. And so I analyzed in great detail statistics for the 2016-17 NBA season, and found far more interesting results than I could have ever expected – far more than just a decision on who should be MVP. Let me preface my results by saying I am a novice when it comes to excessive work in Excel and statistical regressions. That being said my methodology was as described:
- Downloading every player data from basketball-reference (both regular and ‘advanced’, as well as player salaries), and some data from NBA.com
- Proceeding to find team averages, 5-Man averages (top 5 players who started the most games per team), and then averages per position per team (done as a weighted average based upon minutes played per game per player)
- Creating a correlation matrix comparing these Overall Averages to Team Win Percentage
- Using my compiled data to analyze a variety of other factors contributing to team success
Does more money on the team mean more wins? Does more money for starting 5?
Our low R^2 value of .1034 shows there is a very weak relationship for total money spent per team and a team’s regular season win percentage. While the correlation is noticeably higher for the 5-Man average, it remains relatively weak.
In light of “Ball-hog” arguments…How do possession and touches matter?
They don’t really. I looked at a so-called “star player” as the player who had the most possession per team and subtracted that value 5-Man average of possession time. The question then arose for me, “but what if there is a large amount of variance on the team in terms of possession?” This had a marginally higher correlation but overall extremely weak.
Further arguments I have heard is that it’s all about what players and teams do when they have the ball in their hands, and so whoever has the most points per touch should be a better team. As logical as this might seem to some people, my data didn’t really support it. I even took into account that some teams have more possessions than others and adjusted by the ‘Pace’ statistic provided by ESPN.
So what correlations, based on the averages described above, had the greatest correlations with team wins?
The table below shows the values having the highest R^2 values. The darker the color of green represents a higher R^2 value. I then decided to graph these correlations in the form of a scatter plot. I have included the most interesting ones here.
Team average in Win Shares has a ‘strong explanation’ for Team Wins. While 5-Man Average WS/48 has a lower correlation, there is a blatant outlier which is Minnesota. If we remove this team from the data we get an R^2 value of .8727
However, even superior to 5-Man WS/48 (with Minnesota) is VORP and reaches an R^2 value of .89 once we exclude Minnesota, showing a remarkable degree of accuracy and likely predictive power for team win percentage. Additionally, it is interesting to note that the best-fit line shows where a team’s average VORP should be given its win percentage. This implies that those sitting below the line have actually overachieved to reach their team win percentage given their actual team average VORP.
So, after all this who should be MVP?
Well based upon what I’ve found, a star player dominating possession doesn’t really matter. And if we look to VORP we can see that it’s explanatory power is somewhere around 86.42%. I then tried to combine team average VORP with pretty much all other possibilities to try and get a higher R^2 value with p-values being rejected at a .05 alpha level for anything other than team average VORP. But this was in vein. I then decided to run a regression for VORP and 5-Man average Win Shares/48 without Minnesota’s data. I did this because simply by judging by how the graphs looked; Minnesota seemed to be an outlier. (NOTE: Team average VORP in particular does extremely well as an explanation for team win percentage, even without excluding Minnesota. I wanted to get something higher into the 90s) I removed the data and ran the regression and both p-values were substantially low to determine significance and I got an R^2 value of .91307. The coefficients became approximately 1.5 for WS/48 and .25 for VORP. These coefficients are important because I then decided to operate under my biggest assumption: The MVP should be the player who helps his team the most to victory.
Thus, I applied the coefficients of 1.5 and .25 for each player’s WS/48 and VORP, respectively. However, after doing this it didn’t seem enough. I wanted to get rid of influence of the rest of the team players and decided to subtract ultimately use this formula to determine the player’s value:
([Player WS/48] * 1.5 – [5-Man avg. WS/48] * 1.5) + ([Player VORP] * .25 - [Team avg VORP * .25]) = Player Value
The top results are as follows, the left is team adjusted. The right table is not.
It is more or less the same, but guys like Draymond Green aren’t on the first list likely due to their team being so good as a whole. Kawhi Leonard goes up from the first list to the second likely for the same reasoning.
Now this certainly foolproof method to determine an MVP, and I have only used data from this season. That being said, an R^2 value being so high is remarkable. Though there is some unaccounted for, even if we give the 10% or so of ‘unaccounted for data’ to second place James Harden, and take it away from Russell Westbrook. We still see Westbrook at 2.808 and Harden 2.365, indicating to me Westbrook is a deserving MVP candidate at the very least and should, in my eyes with this analysis, be the MVP.
Are players paid representative of calculated player value?
The following table lists the top 30 player values by salary for the year from basketball-reference.com. And the graph determines that the correlation is extremely weak.
Value in great point guards, good centers, and small forwards getting ORBs?
Last of all for my own interest I looked to calculate further regressions for significance, which I am not entirely sure about. I calculated regression of each type of average I compiled versus team wins for advanced stats and basic stats from basketball reference to see what was significant at an alpha level of .05. Once I looked at all ‘rejected’ values from the 14 regression tests I found only 8 which I then ran a regression test for them against team win percentages too and came to this regression output:
My interpretation (albeit with limited knowledge) would say that the collective Point Guard unit was the most important position for a team to have excel. In addition, the Center position demonstrated the belief that many have in that the disparity between a good center and an average center makes having a good one quite valuable. And lastly having a small forward grabbing offensive rebounds is important. The lowest p-value implies that a Point Guard unit’s WS/48 is most valuable and indicative of winning games of the averages and statistics I ran regressions on.
Posted Jun 3, 2017, 5:26 PM UTC · edited