Goal Rates better than Corsi/Fenwick in Player Evaluation
The general consensus among advanced hockey statistic analyzers and is that corsi/fenwick stats are the best statistic for measuring player and team talent levels. For those of you who are not aware of corsi and fenwick let me give you a quick definition. Corsi numbers are the number of shots directed at the goal and include shots, missed shots and blocked shots. Fenwick numbers are the same except it does not included blocked shots (just shots and missed shots). I generally look at fenwick and will do that here but fenwick and corsi are very highly correlated to the results would be similar if I used corsi.
The belief by many that support corsi and fenwick is that by looking at fenwick +/- or fenwick ratio (i.e. fenwick for /(fenwick for + fenwick against)) is an indication of which team is controlling the play and the team that controls the play more will, over time, score the most goals and thus win the most games. There is some good evidence to support this, and controlling the play does go a long way to controlling the score board. The problem I have with many corsi/fenwick enthusiasts is that they often dismiss the influence that ability to drive or suppress shooting percentage plays in the equation. Many dismiss it outright, others feel it has so little impact it isn’t worth considering except when considering outliers or special cases. In this article I am going to take an in depth look at the two and their influence on scoring goals on an individual level.
I have taken that last 4 seasons of 5v5 even strength data and pulled out all the forwards that have played at minimum 2000 minutes of 5v5 ice time over the past 4 seasons. There were a total 310 forwards matching that criteria and for those players I calculated the fenwick shooting percentage (goals / fenwick for), fenwick for rate (FenF20 – fenwick for per 20 minutes of ice time) and goal scoring rate (gf20 – goal for per 20 minutes ice time) while the player was on the ice. What we find is shooting percentage is more correlated with goal production than fenwick rate.
|Shooting % vs GF20||R^2 = 0.8272|
|FenF20 vs GF20||R^2 = 0.4657|
|Shooting % vs FenF20||R^2 = 0.1049|
As you can see, shooting percentage is much more highly correlated with goal scoring rate than fenwick rate is which would seem to indicate that being able to drive shooting percentage is more important for scoring goals than taking a lot of shots.
Here is a list of the top 20 and bottom 10 players in fenwick shooting percentage and fenwick rate.
|1||MARIAN GABORIK||8.07||HENRIK ZETTERBERG||16.7|
|2||SIDNEY CROSBY||7.83||ALEX OVECHKIN||16.3|
|3||ALEX TANGUAY||7.64||PAVEL DATSYUK||16.15|
|4||HENRIK SEDIN||7.63||TOMAS HOLMSTROM||16.05|
|5||BOBBY RYAN||7.60||NICKLAS BACKSTROM||16.05|
|6||STEVE DOWNIE||7.58||ERIC STAAL||16.04|
|7||EVGENI MALKIN||7.57||RYANE CLOWE||15.91|
|8||DANIEL SEDIN||7.55||ALEXANDER SEMIN||15.85|
|9||ILYA KOVALCHUK||7.49||SCOTT GOMEZ||15.8|
|10||NATHAN HORTON||7.44||ZACH PARISE||15.8|
|11||J.P. DUMONT||7.43||ALEXEI PONIKAROVSKY||15.79|
|12||JASON SPEZZA||7.39||JOHAN FRANZEN||15.78|
|13||PAUL STASTNY||7.36||JIRI HUDLER||15.74|
|14||PAVOL DEMITRA||7.33||DAN CLEARY||15.71|
|15||DANY HEATLEY||7.30||SIDNEY CROSBY||15.71|
|16||RYAN MALONE||7.29||JUSTIN WILLIAMS||15.68|
|17||JONATHAN TOEWS||7.28||CHRIS KUNITZ||15.61|
|18||THOMAS VANEK||7.24||MIKHAIL GRABOVSKI||15.56|
|19||SERGEI KOSTITSYN||7.24||JOE PAVELSKI||15.43|
|20||DREW STAFFORD||7.24||MIKAEL SAMUELSSON||15.39|
|301||BLAIR BETTS||4.20||CHUCK KOBASEW||11.34|
|302||ERIC NYSTROM||4.12||TRAVIS MOEN||11.31|
|303||SAMUEL PAHLSSON||4.10||IAN LAPERRIERE||11.23|
|304||SHAWN THORNTON||3.99||ERIC NYSTROM||11.21|
|305||TRAVIS MOEN||3.89||ROB NIEDERMAYER||10.94|
|306||TODD MARCHANT||3.88||TODD MARCHANT||10.91|
|307||NATE THOMPSON||3.75||SAMUEL PAHLSSON||10.87|
|308||FREDRIK SJOSTROM||3.70||JERRED SMITHSON||10.76|
|309||CRAIG ADAMS||3.52||JAY PANDOLFO||10.74|
|310||STEPHANE VEILLEUX||3.49||BLAIR BETTS||10.67|
For both lists, the players are the top of the list are for the most part considered top offensive players and the players at the bottom of the list are not even close to being considered quality offensive players. So, it seems that both shooting percentage and fenwick do a reasonable job at identifying offensively talented players. That said, the FenF20 list includes 7 players (Zetterberg, Datsyuk, Holmstrom, Franzen, Hudler, Cleary and Samuelsson) who have played mostly or fully with the Detroit Red Wings and it seems unlikely to me that 7 of the top 20 offensive players are Red Wing players. Furthermore, the fenwick list also includes guys like Ponikarovsky, Samuelsson, Hudler, Cleary, Williams, etc. who would probably be considered secondary offensive players at best. From just this cursory overview it seems to confirm what we saw with the correlations – Shooting Percentage is a better indicator of offensive talent than Fenwick For rates.
It is actually no surprise that the Red Wings dominate the fenwick rate leader board because the Red Wings organizational philosophy is all about puck control.
“It’s funny because our game looks at numbers just like other games,” says Red Wings general manager Ken Holland, “but as much value as we assign to puck possession and how essential it is to winning, we really don’t have a numerical value for it that everyone can agree on. Remember when [A’s general manager] Billy Beane started emphasizing on-base percentage in baseball? It wasn’t just a curious number; it changed the game. It redefined the type of player you wanted on your team. It’s coming in hockey; we just have to figure out how.”
This got the pro-corsi crowd riled up a bit as they said “Umm, yeah, we have that stat and it is called corsi” and were a bit bewildered at why NHL GMs didn’t make that recognition. But anyway, what the above shows is that an organization that focuses on puck control dominates the corsi for statistic so I guess what that shows is that corsi/fenwick probably is a good measure of puck control. But, as we have seen, fenwick (i.e. puck control) doesn’t automatically translate into goals scored. There are no Red Wing players among the top 20 in fenwick shooting percentage and Datsyuk is the only Red Wing player in the top 20 in goals for per 20 minutes so while they take a lot of shots (or at least shot attempts), they aren’t the best at converting them into goals.
For me, and I am sure many others, the above is enough to conclude that shooting percentage matters a lot in scoring goals, but for the staunch corsi supporters they will argue that corsi is more persistent from season to season and thus is a better predictor of future performance. So which is the better predictor of future performance? The following table shows the correlation between shooting percentage and fenwick rate with the following seasons goal scoring rate.
|Year(s) vs Year(s)||FenSh% to GF20||FenF20 to GF20|
|200708 vs 200809||0.253||0.396|
|200809 vs 200910||0.327||0.434|
|200910 vs 201011||0.317||0.516|
|200709 vs 200911 (2yr)||0.479||0.498|
|200709 vs 200910 (2yr vs 1yr)||0.375||0.479|
Note: For the above season(s) vs season(s) correlation calculations, only players with at least 500 5v5 even strength minutes in each of the four seasons are included. This way the same players are included in all season(s) vs season(s) correlation calculations.
As you can see, when dealing with a single season of data the correlation with GF20 is much better for fenwick rate than for fenwick shooting percentage. The gap closes when using 2 seasons as the predictor of a single season and is almost gone when using 2 seasons to predict the following 2 seasons. It seems that the benefit of using corsi over shooting percentage diminishes to near zero when we have multiple seasons of data and though I haven’t tested it shooting percentage probably has an edge in player evaluation with 3 years of data.
Of course, you would never want to use shooting percentage as a predictor of future goal scoring rate when you could simply use past goal scoring rate as the predictor. Past goal scoring rate has the same ‘small sample size’ limitations as shooting percentage (both use goals scored as it sample size limitation) but scoring rate combines the prediction benefits of shooting percentage and fenwick rate. The table below is the same as above but I have added in GF20 as a predictor.
|Year(s) vs Year(s)||FenSh% to GF20||FenF20 to GF20||GF20 to GF20|
|200708 vs 200809||0.253||0.396||0.386|
|200809 vs 200910||0.327||0.434||0.468|
|200910 vs 201011||0.317||0.516||0.491|
|200709 vs 200911 (2yr)||0.479||0.498||0.619|
|200709 vs 200910 (2yr vs 1yr)||0.375||0.479||0.527|
The above table tells you everything you need to know. When looking at single seasons both GF20 and FenF20 perform similarly at predicting next seasons GF20 with fenwick shooting percentage well behind but when we have 2 years of data as the starting point, GF20 is the clear leader. This means, when we have at least a full seasons worth of data (or approximately 500 minutes ice time), goal scoring rates are as good or better than corsi rates as a predictor of future performance and beyond a years worth of data the benefits increase. When dealing with less than a full season of data, corsi/fenwick may still be the preferred stat when evaluating offensive performance.
So what about the defensive side of things?
|Year(s) vs Year(s)||FenA20 to GA20||GA20 to GA20|
|200708 vs 200809||0.265||0.557|
|200809 vs 200910||0.030||0.360|
|200910 vs 201011||0.120||0.470|
|200709 vs 200911 (2yr)||-0.037||0.371|
|200709 vs 200910 (2yr vs 1yr)||0.000||0.316|
Defensively, fenwick against rate is very poorly correlated with future goals against rate and it gets worse, to the point of complete uselessness, when we consider more seasons. Past goals against rate is a far better predictor of future goals against rate.
Where it gets interest is unlike offense correlation drops when you consider more seasons which seems a bit strange. My guess is the reason we are seeing this is because I am just looking at forwards and defense is more driven by goaltending and defensemen and as more time passes the greater the difference are in goalie and defensemen teammates. Furthermore, forward ice time is largely driven by offensive ability (and not defensive ability) so many of the quality defensive forwards may be removed from the study because of the 500 minute per season minimum I am using (i.e. the group of players used in this study are biased towards those that aren’t focusing on defense). Further analysis is necessary to show either of these as true though but the conclusion to draw from the above table that, for forwards at least, goals against rates are by far the better indicator of defensive ability.
In summary, it should be clear that we cannot simply ignore the impact of a players ability to drive or suppress shooting percentage in the individual player performance evaluation and so long as you have a full year of data (or > 500 or more minutes ice time) the preferred stat for individual player performance evaluation should be goal scoring rate. Corsi/fenwick likely only provide a benefit to individual performance evaluation when dealing with less than a full year of data.