What NHL Teams Are Under or Over Performing?
Former NFL coach Bill Parcells once wisely said — “you are what your record says you are”. Ultimately this is true. By the end of the NHL season, I think there is truly a large enough sample of games where every team deserves their place in the standings. But in short samples, sometimes teams may overperform or underperform their record. I want to take a look at some team level statistics and see what correlates highly with a team’s record (given the weirdness of the NHL point system where the loser point exists for OT losses, a team’s record = their points %). Then I want to take a look at the current season as of 3/18/2021, where most teams are at around the 30 game mark, and see if I can spot any teams that are outperforming or underperforming their record.
I’ll start by grabbing data from Natural Stat Trick. If you are curious how I do this, I have another blog post about how to scrape Natural Stat Trick here. . I’m taking team stats from the 2013–2014 season up to last season. I will compare CF%, FF%, SF%, ‘GF%’, ‘xGF%’, ‘SCF%’, ‘SCSF%’, ‘HDCF%’,’HDSF%’, ‘SH%’, ‘SV%’, ‘PDO’. If you are unfamiliar with any of these stats, check out the glossary on Natural Stat Trick.
Onto the analysis! Looking at the correlation heatmap below, the top metrics that correlate with team record are GF%, PDO, SH%, and then xGF%.
It’s no surprise that GF% is so highly correlated with record given that a game result is a function of goal differential of 1 game and GF% is a function of goal differential across multiple games.
GF% - Percentage of total Goals in games that team played that are for that team. GF*100/(GF+GA)
xGF% is the best metric that does not include actual goals scored in its calculation. xGF% gauges the scoring opportunity generation and suppression of the skaters independent of goaltending ability for either team. The calculation for xGF% is derived from a model. Information for this metric can be found here.
PDO — Shooting percentage plus save percentage. (GF/SF)+(GA/SA)
PDO is very strongly correlated with Point %. There is some debate as to whether PDO is luck based and thats team should regress to 100. In my opinion teams can sustain PDO above or below 100 depending on goaltending skill and some degree of shooting ability or shot selection. However, given the max and min season PDO for the period measured of 104 and 96 accordingly, it’s probable a team that has a PDO far outside that range will regress into that range over the course of the season. PDO is normally distributed with a mean of 1. There is only a .16% probability of a team finishing the season outside the range of .96–1.04 which is 3.15 standard deviations away from the mean in both directions.
I regressed GF% and against Points %. I also combined PDO and xGF% in a multiple linear regression model. It makes sense to combine these two given they measure difference components of team success and PDO has a very low correlation with xGF%, thus avoiding any multicollinearity issues.
Using stats model library in Python to fit and evaluate the data. I can see GF% has an R² .93 explaining 93% of the variability in Points %. The PDO and xGF% model has an R² of .89, so these features together explain 89% of the variability in Points %.
Fitting the coefficients onto data from the current season, and by looking at the difference between actual points and points inferred from the model (AKA the residuals), I can see which teams records maybe don’t reflect their performance completely.
Based on GF%, Rangers, Kings, and Stars, standout as have a worse record than expected and the Avalanche and Panthers stand out as having a better record than expected. (Though of note, the Rangers thrashed the Flyers 9–0 last night, so it’s possible if I wrote this a few days ago they wouldn’t seem such a steep under-performer)
Looking at the residuals for the multiple regression on xGF% and PDO, the Rangers and Stars again show up as big under-performers while the Blue are the only overperformer that stands out though no team is overperforming by more than 10% points.
What conclusions can be drawn from this? I think the strongest evidence points to the Rangers and Stars (both at .500 Points %) are better than their records would indicate. Likely, if they performed to the same level rest of season, they would outperform their current Points %. Both are on the outside of the playoffs in their division, but I wouldn’t be surprised if either can make up enough ground to challenge for a spot by the end of season barring a significant roster change.
