After some advice I was given from the previous posts on the Francoso statistic, I’ve decided to see how the statistic compares to more modern methods of predicting the outcome of a hockey game.

As a reminder, Francoso % is a way of quantifying a team’s success at gaining possession of the puck in their own defensive zone and transitioning the puck into their opponent’s half of the ice without giving the other team possession or committing an icing penalty.

In Moneypuck, parts 1 and 2, I compared Francoso % to common hockey statistics: shots for, shots against, and face-off percentage. While these stats might be commonly-known and well-understood, they’re actually not all that closely related to whether or not a team wins a hockey game. You got a little evidence of that when you saw how poorly they predicted the outcomes of the games in my (admittedly small) sample from the last post. So maybe I was stacking the deck in favour of my little pet project.

These days, teams rely on more advanced analytics. Stats like Corsi, Fenwick, and Zone Face-off Win% are more commonly discussed when evaluating the quality of a team. Statisticians have even developed a way of quantifying how lucky a team is during a game or across the season by calculating Shooting Plus Save Percentage.

Corsi and Fenwick percentage are the percentage of the total shots in the game each team took at goal (not just on-goal) during even-strength play (usually 5-on-5 players). For Corsi, this total includes shots on net, wide of the net, and blocked shots. For Fenwick, the total excludes blocked shots. These stats are ways of quantifying the percentage of total scoring chances a team had in the game. There are plusses and minuses to using either stat. For Corsi, there is a larger number of events (shots) to sample from, so it’s a more stable number, but for Fenwick, excluding blocked shots ensures defensemen aren’t penalised because they’re good shot blockers.

Zone Face-off Win% is a refined way of quantifying the value of obtaining puck possession when play restarts. Offensive and Defensive Zone Face-off Win% exclude face-offs in the neutral zone where obtaining possession isn’t critical. According to some really neat research from Statistical Sports Consulting LLC, a hockey player must win 76 more face-offs than he loses in order to net the equivalent of an additional goal for his team. In the offensive or defensive zone, this number reduces to about 60. As a result, Zone Face-off Win% should be a better predictor of whether a team wins a game than overall Face-off Win%.

Finally, we have our “puck luck” statistic, Shooting Plus Save Percentage (SPSV%). This statistic is a bit difficult to understand, but it was created as a way of quantifying the extent to which a team is on a good or bad streak of luck. It’s a way of quantifying the impact a few bad or good bounces has on your team’s performance. The stat is simply the team’s even-strength shooting percentage plus their goalies’ even-strength save percentage, usually expressed out of 1000. For example, if 2.80% of your team’s shots went in, and your goalie saved 93.00% of shots against, your team’s SPSV% would be 9580.

This stat quantifies luck because if you were to total up every team’s shot% and the opposing teams’ corresponding save% in the corresponding games, the overall SPSV% would be 1000. Logically, all teams in the league are proportionally distributed above or below this number and the league average is always 1000. Accordingly, teams that have a SPSV% consistently above 1000 are considered “lucky” and those that have a SPSV% consistently below are considered “unlucky”.

But how do these advanced statistics compare to Francoso % in terms of predicting the outcome of a hockey game? I’ve increased my sample size a bit and pitted Francoso against each of these stats. I still don’t have enough data to have a reliable predictive model, but we can at least take a look at the trends so far.