Examining Player Development in NCAA DI Women’s Hockey with Game Score Pt. 2

Continued from Pt. 1

When do women’s hockey players reach their peak? How do they develop? These questions may sound straightforward, but they are exceedingly difficult to answer because of the finite opportunities for players to pursue high-level post-collegiate hockey. There is no consensus “top” professional league in the world, and major international tournaments are brief; conclusions we draw from them can be heavily skewed by the group format.

For all these reasons and more, NCAA DI (Division I) is a logical place to explore player development. It is data-rich, relative to the rest of women’s hockey, and Carleen Markey’s work with aging curves placed CWHL (Canadian Women’s Hockey League) skaters’ peak offensive production between the ages of 22 and 23. That falls within the range of many collegiate careers.

Credit: Carleen Markey

The Pipeline

The zenith of skill and competition in the world of women’s hockey are the Olympics and the IIHF Women’s World Championship. These tournaments are filled with, and often dominated by, active DI players and alumnae. As one might expect, the majority of those players represent Team USA and Team Canada.

At the 2019 Worlds in Espoo, Finland, all of Team USA’s roster and 20 of the 23 players on Team Canada spent at least one year in an NCAA DI program, compared to just five of the 23 players on Team Finland’s silver medal-winning team, and one player on Team Russia’s fourth-place team. 

That said, there are more international players playing college hockey in North America every year. Per biographical data on EliteProspects.com, the ratio of international players in DI hockey climbed from 4.17 percent in 2015-16 to 5.07 percent in 2019-20.

Those percentages don’t mean much without the context of the women’s hockey landscape across the globe. According to the IIHF, there are 88,732 registered female players in Canada and 82,808 in the U.S. Outside of North America, there are 26,381 registered players in Sweden, Finland, Czech Republic, Russia, France, Germany, Switzerland, Japan, and Norway combined.

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Examining Player Development in NCAA DI Women’s Hockey with Game Score Pt. 1

Carleen Markey broke new ground with her presentation on women’s hockey aging curves in the CWHL (Canadian Women’s Hockey League) at RITSAC 2019. Her work, which was built from the scaffolding of the Evolving Wild twins’ aging curves, established that offensive production among CWHL skaters peaked around age 22 to 23. That work by Markey got me thinking about how players developed just before going pro in North America and Europe, and/or becoming fixtures on national teams.

So, I set my eyes on NCAA DI (Division I) women’s hockey.

DI schools have served as the primary pipeline of talent for Team Canada and Team USA for decades. Furthermore, DI schools have served as a valuable proving ground for many of the most talented European players in the world. With Carleen’s work in mind, I set out to analyze how skaters developed in DI hockey before they reached their peak production years and their athletic prime.

Approach 

The greatest obstacle to any statistical analysis of the women’s game is the scarcity of public data. Fortunately, NCAA DI is something of an exception because of sites like collegehockeystats.net, collegehockeynews.com, and the database on HockeyEastOnline.com.

I decided on developing a game score for DI hockey to serve as an all-in-one stat that could provide a rough measure of a player’s overall impact or value. Dom Luszczyszyn first applied game score to hockey, and his work provided a framework. Creating game score for DI hockey was also appealing because I was able to apply lessons learned from working with Shawn Ferris’ NWHL (National Women’s Hockey League) game score. At the time, this sounded like fewer headaches for me. I was wrong; I had forgotten how many headaches there were the first go around.

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Introducing NWHLe and Translation Factors

In April 2017, Rob Vollman tweeted out what he called “rough and preliminary” translation factors for women’s hockey. At the time, I was playing around with counting stats from two years of NWHL and CWHL hockey, and wanted to develop as many tools and resources as I could to better understand the women’s game. Curious to know what the competitive landscape of post-collegiate hockey looked like in North America and elsewhere, I began to keep track of data with the intention of building on Rob’s translation factors.

The world of women’s hockey in North America has changed dramatically in the three years since Rob’s tweet. My initial plans went up in smoke when the CWHL suddenly folded after the 2018-19 season. As a result, I shifted my focus to developing NWHL equivalency factors – or NWHLe – for NCAA DI, NCAA DIII, and USports. Unfortunately, it quickly became apparent that the sample size of USports alumnae to play a significant number of games in the NWHL was too small to work with.

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Revisiting NWHL Game Score

In March 2018, Shawn Ferris of Hockey Graphs introduced his NWHL Game Score, which was based on Dom Luszczyszyn’s NHL Game Score. It was groundbreaking work in women’s hockey analytics, which is still very much in its infancy — especially at the professional level.

Game score is a valuable tool that can give us a better understanding of a player’s performance than points for skaters or save percentage and goals against average for goaltenders. It provides us with a single value that incorporates relevant points of data which we can use to compare the performances of two or more players in a single game or over the course of many games, including seasons and careers.

As Shawn noted in his work, game score is particularly valuable for analyzing performance in the NWHL because of the brevity of the regular season. Through the league’s first four seasons, the average length of a season was under 18 games. The 2019-20 season promises a schedule of 24 games, which is still less than a third of the length of the NHL season. That brief schedule creates an opportunity for shooting percentage factors to influence both a players’ production and our perception of their performance.

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