HOW SCSV% CAN BE THE 'ADVANCED STAT' OF CHOICE FOR MINOR HOCKEY COACHES & PLAYERS
Advanced stats are all the rage in professional and high-end amateur hockey leagues right now. But those data collection processes are highly sophisticated and costly to run, so what can the average minor hockey coach or player use to gain similar perspective?
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In 2015-16, my first season as an Assistant Coach at the University of Waterloo, I took on the task of creating and leading an enhanced statistics collection and analysis program for the team. Through discussion with the coaching staff, I landed on faceoff wins/losses by location, turnovers/takeaways by location, shots on net percentage, and quality of scoring chances (both our team and our competitor), as key indicators of success, and relatively easy data points to track.
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This took three of our scratched players to track each game, which is not a realistic option in the world of minor hockey.
In the advanced stats world of the NHL, they use 20+ data points, plus analysis of thousands of games, to evaluate and assign value to the quality of a shot.
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That makes the best place to start with this, understanding what goes into the advanced stats, Expected Goals (xG) and Goals Saved Above Expected (GSAx).
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What is xG & GSAx? – An excerpt from an article by Jake Zrihen of the Hockey Writers (Click Here to read the whole article)
To understand the concept of goals saved above expected, one must first understand what expected goals (xG) are. The calculation behind them is relatively simple. Each shot attempt is assigned a numerical value based on the percent chance the shot has of going in. This percentage is based on an abundance of factors, including but not limited to shot distance, shot angle, goalie positioning, number of defensemen present, and number of sticks in the way. Even factors like whether or not there was a cross-crease pass preceding the shot are taken into account.
That percentage is then converted to a decimal (a shot with a 20% chance of going in has an xG value of 0.2). These decimals are accumulated into a team’s or player’s xG total in a game. For example, let’s say Sidney Crosby takes three shot attempts in a game. The first has a 15% chance of going in, the second has a 32% chance, and the third has a 56% chance. Crosby’s xG total for the game would be the total of all three percentages or 1.03 xG.
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Applying this concept to goaltending is pretty simple. Let’s say Sergei Bobrovsky plays a game against the Carolina Hurricanes. He allows three goals against, but Carolina’s xG total for the game is only 2.42. To determine Bobrovsky’s GSAx, we subtract 3 from 2.42. His GSAx for the game is -0.58. Not a very good game.
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Now, we simply don’t have the ability to collect data and analyze it at this level, so my thought process turned to streamlining stats collection and analysis down to a single item that would paint a strong picture for minor hockey coaches and players as to what their strengths and weaknesses are, with a direct correlation to game outcomes.
What I eventually landed on was one category; scoring chances. Scoring chances as they relate to a goalie’s success with them, and a team’s ability to create and convert them, while stopping the other team from generating them.
This led me to the Scoring Chances Save Percentage (SCSV%) statistical measurement.
What is SCSV% and how is it like GSAx?
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Stemming from the same Royal Road theory that xG and GSAx was born from, a theory created by former NHL Goaltender turned analyst and statistician Steve Valiquette of CSA Analytics, Scoring Chance Save Percentage (SCSV%) can differentiate a goalie’s ability to stop high quality chances, from regular quality chances. This stat can also be used to determine what type and at what frequency your team allows, generates and converts on high quality scoring chances, providing guidance for areas of focus in team development sessions.
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And it can all be tracked by a single parent volunteer during each game.
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Now to the nitty gritty of collecting the data. When we look at the spreadsheet below, it’s important to remember that we’re using a standard set of data points to define our scoring chances, which makes it of utmost importance to have a parent/coach who knows what they’re watching, tracking these stats at each game.
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The spreadsheet below shows the data points we’ll focus on collecting.
Now we need to break down the scoring chance categories, keeping in mind that these are all related to being in the offensive zone at 5v5.
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Carried across mid-ice - a puck that is carried by the shooter, across the middle of the ice, below the tops of the circles
Pass across mid-ice - a puck that is passed across the middle of the ice, below or into the area below tops of circles, prior to shot
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Pass/shot from below goal line - a shot on net (e.g. wraparound) or pass to quick shot from above goal line
Shot w traffic - any shot where the goalie’s vision is impaired by bodies moving in front of them, or puck changes direction after initial shot release
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Breakaway - in-game breakaways aren’t always 'advantage shooter' but the odds of scoring definitely increase in this situation (this does not include penalty shots which are independent of 5v5 play and provide the shooter with much higher odds)
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Rebound - shots as a direct result of a rebound, whether off the goalie or off of a player in front of them
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All other shots - general shots, recorded here for the purpose of totaling with scoring chances to get overall SV%
Lets dig into the task of creating xG and GSAx type results from these data points. The average SV% of the top goaltenders in the NHL is .915, and by my analysis, anything above .890 in SCSV% should be considered good.
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To get an xG type result for your team for scoring chances alone, total the number of scoring chances you generated and multiply by .11 (the remaining percentage above an average SCSV%) and you’ll get your total.
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For example, say you generated 15 scoring chances in the game and scored twice. 15 x .11 = 1.65, which means your team overachieved, or had a positive xG for the game of .35, by scoring twice. Over the course of a 20 game analysis period, if your team continued at this scoring clip, you would have amassed 7 more goals than expected from your scoring chances.
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So using the same circumstances as above, if your goaltender faced 15 scoring chances in the game and ended with a .900 SCSV%, which leaves .1 of a goal remaining, the calculation would be, 15 x .1 = 1.5, meaning your goaltender saved you .15 of a goal. And while that may not seem like much, if your goaltender were to continue hitting a .900 SCSV% over a 20 game span where they faced 15 scoring chances per game, at the end of the 20 game stretch they would have saved you 3 goals.
In order to come to a final GSAx type stat for your goaltenders overall performance you would do the same calculations with their SV% on all non-scoring chance shots. For this example we'll use the NHL number of .915 to keep things simple. That would mean that your goaltender was neither positive nor negative in that statistical category over the 20 game stretch, so the calculation would be 3 + 0 = 3 goals saved above expected, or GSAx.
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Taking this one step further, you can total your new xG number (7) with your goalies GSAx (3) to see that your team as a whole has managed to overachieve against expected by 10 goals.
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Your next move after discovering this stat might be to analyze what types of scoring chances are working and what aren't, to see if you can improve this total even more. Then you could look to what types of chances you're giving up most, and how could you add to your defensive strategy to tighten up on those.
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It’s important to differentiate 5v5 stats from special team’s situations as well, when collecting data. From a defensive standpoint, all shots against on a penalty kill could be considered high quality (scoring) chances, while the same could be said when your team has a power play. For us, special team’s represents a separate data collection exercise all together.
This long term collection and analysis of data will allow us to pinpoint trends in a a goalie’s game that they may be struggling with, such as chances generated from below the goal line, or rebound control.
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There are also several ways in which these stats can be enhanced by increasing our data collection efforts, allowing us to track things such as the success of a particular line combination at generating scoring chances over an extended period, or a five-player unit/defensive pairs’ ability to limit scoring chances.
Thanks for taking the time!
Jory Elliott – Founder & Head Coach, SHFT Hockey
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Visit our contact page HERE to request a copy of the original spreadsheet featured above.