So Wins Produced does connect back to team wins.
But remember, it also connects all the box score statistics to team wins. So if you really think the best players are just the top scorers, Wins Produced is going to sometimes contradict what you think. Again, the box score tells you more than who scores the points. It also tells you -- if you look at everything -- how each player contributed to team success. But to hear that story, you need to look at the entire box score.
The Storm won only 10 games in 2015, a mark that was only better than the San Antonio Stars. And the team's Wins Produced sums to 10.5. So again, these marks are quite close.
And this is the typical result. The following table shows the difference between actual wins and team Wins Produced for all WNBA teams in 2015. Again, these tend to be very similar values.
A few notes on this table:
An average player will post a WP40 -- or Wins Produced per 40 minutes -- of 0.100. This should make sense. The average team will win 0.500 games per 40 minutes. So an average player would produced 0.100 wins in that time.
Notre Dame had four players -- Allen, Turner, Cable, and Huffman -- who were twice as good as average. Loyd was above average, but not quite as productive as these players per 40 minutes played. So Loyd was not the "best" (but still, very good!).
ADJP40 represents a player's contribution to wins before any adjustment is made for position played. Because centers and power forwards tend to accumulate rebounds and tend not to commit turnovers (and guards tend to be the opposite), player performance has to evaluated relative to the position played. So WP40 -- or Wins Produced per 40 minutes -- is simply ADJP40 adjusted for position played.
Notre Dame won 36 games in 2014-15. As one can see, the summation of Wins Produced for this team is 34. In other words -- as is generally the case -- team wins and player Wins Produced are often quite similar.
Wins Produced explains how each player impacts team wins. If you wish to forecast with this model, you need to note all the adjustments made. These include adjustments for position played and the teammates' production of assists and defensive rebounds. Unpacking all these adjustments is difficult (and as far as I know, has never been done by anyone trying to forecast with this model).
As noted, Wins Produced can be calculated for any basketball league. And here at the Ladies League -- thanks to the efforts of Lindsey Darvin and Taylor Cella -- we have the entire history of the WNBA (the raw numbers used to calculate Wins Produced can be found at basketball-reference.com and WNBA.com). So we can see what Jewell Loyd did in her first season with the Seattle Storm.
The following table re-produces what we report at our site for the Storm in 2015. Again, average WP40 is still 0.100. And as one can see, Loyd was a bit below average as a rookie. Of course, that doesn't mean Loyd will always be around average. Rookies do tend to get better. So Loyd could certainly become an above average player in the future.
The Ladies League is all about creating a space where women can talk about the sports they love. Women make up 30% to 40% of fans. But women are less than 10% of the sports media. So clearly women are not talking about sports in the media as often as women are talking about sports everywhere else.
This is not the only issue, though, with women and sports. The coverage of women's sports is also grossly inadequate. Cooky, Messner, and Musto (2015) have noted that more than 95% of the coverage of sports on ESPN Sportscenter focuses on men's sports. And this pattern extends to data collection. Want to know how many points, rebounds, steals, etc... Karl Anthony-Towns of Kentucky (the number one pick in the 2015 NBA draft) accumulated during the 2014-15 season? ESPN can help you out! Do you want to know the same thing for Jewell Loyd of the Notre Dame, the first pick in the WNBA draft in 2015? Not going to find this at ESPN (although stat.ncaa.org does report these numbers if you know where to look)!
So when it comes to men's sports, the data revolution of the 21st century is in full swing. But women's sports are often treated like we still live in the 1950s and everyone "Likes Ike". You can see the games (sometimes!). But if you want to know who is "good" or "bad".... well, you can just trust your eyes and hope for the best.
The purpose of data analysis in sports is to move past the "staring" approach to player evaluation. Perceptions of performance in women's basketball – as Harris and Berri (2015) – tend to be driven by scoring (which is the same -- as The Wages of Wins argues - that we see in men's basketball). This makes sense since scoring is what stands out when you watch the games. But if you want to know how each player impacts wins, you have to do more. You have to actually analyze some numbers.
For example, consider what Jewell Loyd did for Notre Dame in 2014-15. The Irish won 34 games and advanced to the NCAA Championship game. And the leading scorer on that team was Loyd. So it is not surprising Loyd -- again, the number one pick in the 2015 WNBA draft -- was considered the team's best player. But was Loyd the best?
To answer this question we need determine how each player impacted team wins. As detailed in The Wages of Wins and Stumbling on Wins, the box score statistics collected for players can be linked directly to team wins (the process is detailed here for the NBA)
All of this work tells a one basic story: Wins in basketball (whether played in the NBA, WNBA, or college by men or women) are primarily about gaining and keeping possession of the ball (i.e. rebounds, steals, and turnovers) and turning those possessions into points (i.e. shooting efficiency from the field and line). And that means in evaluating how a player contributes to wins we need to do more than look at scoring totals. We also have to look at shooting efficiency, rebounds, turnovers, and everything else in the box score.
To illustrate, here is what we see when we look at the 2014-15 Notre Dame Fighting Irish (raw numbers used to calculate Wins Produced taken from stats.ncaa.org). Once again, Jewell Loyd was the leading scorer on this team. But when we consider everything in the box score, and connect all of this to teams wins -- as was done to create the following table -- we see that Lindsay Allen and Brianna Turner had a larger impact on team wins.
The Ladies League provides data on each player' s Wins Produced in the history of the WNBA (1997 to 2015). What follows is a brief discussion of this data.
Measuring a Player's Wins Produced for the WNBA