Last week, I introduced the world to my new pet project, the Francoso percentage. This hockey statistic is meant to be my ticket to a high-flying and prestigious career moneypucking the NHL. OK, not really. I realise it’s already been moneypucked. I also realise that it’s going to take a lot more than a sample size of 20 teams to demonstrate whether Francoso has any predictive power worth shaking a (hockey) stick at. But that’s what I’ve got for you—a pilot study with 20 teams. I’ll continue to gather more data as the months go on, but without any volunteers (anyone keen?) to help, it’s going to be slow going because data collection is a manual process that involves actually watching each entire game.

question

Which will be the best predictor of a team winning their game?

Having more shots “on goal” than their opponent Having a better Francoso % than their opponent Having fewer shots against your own net than their opponent Having a better face-off win % than your opponent

At the time this post was published, our poll showed:

17 votes for more shots on goal, 17 votes for better Francoso %, 9 votes for fewer shots against, and 5 votes for better face-off win %.

I also got many interesting and passionate comments on Reddit and in the blog. Some folks offered very valuable advice, critiques, kudos, and resources. Others were unimpressed with the choices I presented in the poll. Still others displayed their excellent hockey trivia knowledge in pointing out that Corsi and Fenwick didn’t name those stats after themselves. Turns out I’m just arrogant.

I very much appreciate each of these types of comments. After all, when it comes to sports statistical analysis, I’m definitely a rookie. In fact, it would be wonderful if I could get some advice from my readers based on the results of the pilot I’ll present below.

Answer

I’m excited to say that so far, having a better Francoso % than your opponent is by far the best predictor of whether your hockey team will win the game. In this sample, 8/10 teams who had a better Francoso % than their opponent went on to win the game. Compare that win rate to having more shots on goal (5/10), fewer shots against (5/10) and a better face-off win% (4/10).

In case you’re interested…

Now I’ll delve into the nitty gritty details. As I’ve been gathering these data, I’ve been considering the many different ways they could (and maybe should) be analysed. So far, partly because the sample size is so small, I’ve refrained from doing any statistical tests aside from simple correlations/regressions to help visually compare the relationships between variables. Nevertheless, I’ve got some fun things to show you.

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. We hypothesise it’s also a good way of predicting whether a team will win the game.

First, Francoso’s predictive power seems to be expressed in a binary fashion rather than in a continuous fashion. In other words, there was only a tiny weak positive relationship between having a high Francoso % per se and winning (shown in the left graph). Rather, the relationship lied in having a higher Francoso % than your opponent (shown on the right).

The implication here is that if teams want to use the concept of Francoso % to their competitive advantage, it’s not simply a matter of making sure their team exits the defensive half of the ice more effectively, it’s also about preventing the other team from penetrating back into your defensive half.

It’s also interesting that the advantage of beating the other team’s Francoso % played out twice as strongly in the first period than it did in the remaining two periods, although there was consistently a positive effect across all periods.

I tried to eek some more statistical power out of my data by investigating the relationship between Francoso % and goals. This approach is suggested in a marvelous blog post by hockeyanalytics.com. I do realise that I’m violating some of the other “laws” discussed in that post, but over time, I’ll rectify that. This is just a pilot, after all.

Anyway, in this small sample, there was a weak negative relationship between goals against and Francoso %, but hardly any relationship (it was weakly negative if anything) between goals for and Francoso %. In other words, teams with higher Francoso % tended to have fewer goals scored against them, but there wasn’t much of a pattern in terms of how many goals they scored. Those results shouldn’t be too surprising because Francoso % is mainly a way of measuring the extent to which a team can exit their half of the ice successfully without coughing up the puck in a potentially dangerous spot.

Note for the statisticians: The regression coefficients in the above images are likely to be inappropriate because I haven’t yet accounted for the fact that each pair of data points are dependent on one-another.

Those are the most interesting trends I can see so far.

I know there is so much more to be done in terms of conducting proper statistical analyses, data collection, and comparing Francoso % against real predictive contenders such as Corsi, Fenwick, PDO (a measure of luck). Obviously, this research has just begun and the conclusions are very much “works in progress,” but I am curious to hear your suggestions about where I should take Francoso next. I really appreciate any advice, resources, or critiques.

How would you collect these data? What statistical approach would you use? What variables should I compare with Francoso? Are there other statistics that seem similar?

Thanks for reading.