Scouting: How to Analyze the Habits of a Player
Evaluating the projectable skills of a player is all about sample size
For the last 3 years, I’ve had the opportunity of helping the McGill Martlets with player evaluation.
I mostly focused my efforts on the CEGEP league, attending dozens of games all across Quebec, each year. Throughout my time at McGill, I had the chance of learning from some brilliant hockey minds and developed a method for streamlining my evaluation process when going to games.
An important thing to remember is that, over time, every scout develops his/her way of doing things. I simply wanted to share my methodology to help newcomers in the field get ideas on how to develop their own approach.
The Use of Advanced Statistics
Whenever I go scouting, I focus on what’s happening on the ice.
However, analytical insights still play a big role in my evaluation of players. My use of analytics is very similar to that of Jack Han as he is the one who taught me how to leverage analytical insights in the context of player evaluation.
This article is focused on my methodology when watching games, but I generally leverage analytics in scouting at 4 different levels:
Level 1: Point Equivalencies/Projections (NHLe-like metrics) – before games
Level 2: Corsi/xG Trend (Play driving metrics) – after games
Level 3: Micro-Stats Breakdown (Event-based metrics) – after games
Level 4: Technical Analysis (Micro-habit metrics) – after games
The First Shift(s)
Even if my focus is on the game, my approach to scouting players has a quantitative basis.
In its simplest form, scouting is a game of sample size. It’s all about observing a player perform an event, a skill or a habit enough times to confidently assess whether it is projectable or not at the next level.
But when going scouting for the first time, it is easy to get overwhelmed by everything happening on the ice simultaneously. That’s why, the key to the first shift(s) is about identifying skills or habits that are important and that players do often.
The importance of habits can be based on a variety of factors and could depend on organizational preferences. But in the technical game that is women’s hockey, there is one skill (set) that is practically mandatory to possess: skating.
By nature, skating is also something that players do often. Inspired by Peter Smith’s approach, that is why, my focus during the first shift(s) of a game is on the skating technique and efficiency of the players I’m there to scout.
Skating technique and efficiency pertains to specific habits such as stride extension, knee flexion, pivot, edge work and crossovers.
Also, a player’s skating habits are almost guaranteed to stay the same throughout a game. It is very rare that a player who has trouble accessing her outside edges at the beginning of a game suddenly learns to skate as smoothly as Kristi Yamaguchi mid-game.
A player might, however, improve her skating technique throughout a season. Thus, even if I have seen a player multiple times already, I always start with her skating to note down any potential improvements.
The Shift Evaluation Process
After the first few shifts, I then move on to watching the skills of a player with the puck and away from it. Whether it be puck reception, stick handling, passing, shooting or defensive play, my approach is consistent with the idea behind our SARAH models.
More specifically, as we had discussed in the past, the SARAH models allowed us to identify skills that are more difficult to develop than others.
Consistent with that idea, I start by trying to identify “easy” projectable habits that players could possess and perform often. I then use the “more difficult” ones to differentiate elite players from the crowd.
The reason I don’t track SARAH habits during in-person viewings is that hockey is a fast game. Video is a much more suitable tool for ensuring that SARAH tracking is accurate (as you can re-watch specific sequences multiple times).
Instead, when attending games, I use a simple shift rating system that captures my overall appreciation of a player’s game. Again, it’s a game of sample size and this method is inspired by that of Stewart McCarthy, our assistant coach at McGill.
If a player demonstrates projectable habits and improves the condition of the puck during the shift, I assign a “+” to that shift. Otherwise, I assign a “-“ to that shift.
In that simple manner, I am able to get an overall rating for players after each game by dividing the number of “positive” shifts by the total number of shifts.
The simplicity of this system allows me to focus on identifying the key strengths and weaknesses of many different players at the same time during a game. In turn, this facilitates qualitative note-taking on the side and allows me to provide summary reports that highlight the main technical characteristics of players to our coaching staff.
Moreover, this avoids me having to constantly look down at a tracking sheet/screen and miss half of what’s happening on the ice.
The simplicity of this system revolving around sample size allows to capture an overall trend of a player’s projectable habits over time.
Then, linking that to levels 2, 3 and 4 of statistical analysis described above, I can make sure that my overall appreciation of a player’s game and the underlying metrics of a player have similar patterns of evolution.
Linking Player Habits to Tactics & Player Development
Finally, one thing that I learned while scouting alongside Katia Clément-Heydra, this past season, is how to link the analysis of technical habits to the big picture.
If a player demonstrates great technique in one particular aspect of her game, but constantly can’t find a way to stack her skills to drive play and improve the condition of the puck, there may be some underlying red flags in her game.
And linking technique to micro-stats (and ultimately tactics) can be an interesting way of assessing and projecting a player’s global contribution to a team. That first part is the premise of the SARAH models.
But, throughout the month of August, we will go one step further by cracking open the “black box” of some SARAH models to better understand the link between habits and events.
Then, we will try to link these habits and events to tactics through advanced statistical modelling and video analysis.