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Hockey Analytics

Midseason Points Distribution

by Gus Katsaros

Most teams are approaching or hit the mid-season mark, with fantasy GMs, analysts – and some nervous team personnel – making visions and projections for second half player and team production. To better assess the NHL at midseason, I like to use a method of ‘points distribution’ among forwards and defensemen.


Using only 5v5 data from Natural Stat Trick with a New Year’s Eve cutoff and differentiating between defensemen and forwards, I created a table that uses 5-point intervals as a grouping or band. I excluded special teams to focus strictly on even strength play, however, this exercise is just as valuable to assess players at 5v4, with the only difference being in the metrics to use. Even strength play and special teams are assessed in different fashion, requiring different metrics.


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A summary table with players listed within the bands is shown below, but this is an extensive set of data and too large to include in a table. To follow along, or even try this out on your own, I have uploaded the workbook to a Google Drive, and if you’d like access, just click on the link here to download a copy  for yourself. The workbook contains a pivot table (an incredible Excel tool that is the foundation for Visualization software like Tableau. The data is updateable via Natural Stat Trick, with newer data if you’d like a snapshot at different times of the season. There are a lot to discover here, and for updated data, just start here at NST. Copy the data into the table tabs and refresh the workbook.



I use this table to look at players among scoring peers and with some of the advanced metrics, I try to determine where players are under or overvalued. Let’s get into some results.


The summary table comprises the far left ‘Distribution’ column outlining the point bands, with splits by position – to keep authenticity and integrity calculating averages within statistical categories – and the number of players contained within each band. The rest of the columns are averages of the category for the band and position.


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For example, there are 11 forwards in the highest point band 30-35, averaging the most time on ice per game (15.41), and earning points on 80.67% of on-ice goals scored, as depicted by their Individual Point Percentage (IPP). When making comparisons to the bands of players in the 25-29 range, the only notable difference between players is the increased IPP. The other observation is the highest amount of hits taken are in the highest points band, coupled by the number of hits being near the bottom. Having the puck – or at least being on the team with increased possession will induce more hits against, while having the puck means they will have to hit less. When you hear about teams ‘outhitting’ their opponents, that usually means the opponents had more control of the puck.


The similar correlation to defensemen in the top two bands are severely skewed due to there being only one player in each of the top bands.


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The 15-19 point band contains some of the typical high end rearguards, with the one exception, Jeff Petry. Only three points shy of his career high at 5v5, he’s included in select company. He’s also laid out 91 hits double the band’s overall average. The absence of an inflated individual or on-ice shooting percentages can point to something different in his play at even strength this season that requires deeper analysis. The Montreal blueliner is having one of the best low-key seasons in the NHL. Ryan McDonagh is the only other blueliner with 13 individual high danger scoring chances.


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The two highest forward point bands are broken down below. It’s not surprising to see Toronto Maple Leafs teammates, John Tavares and Mitch Marner listed among the top point getters, while landing the highest spots in on-ice shooting percentage. Tavares, the former Islander, tops the group with 6.22 shot attempts per 60 minutes, almost double the point band’s average showing his prowess around the net – a distinction not lost on tacticians. Noticeable is the high number on Sidney Crosby (5.29) – with similar dogged determination to be a difference maker close to the net – and the soon-to-be handsomely paid, Brayden Point (5.14).


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To be included among the leaders in the 25-29 band, Vancouver rookie, Elias Petersson generates the least amount of individual high danger scoring chances per 60 minutes, but earned a point on 86.2% of 5v5 goals scored while he was on the ice – courtesy of a 25.5% individual shooting percentage. He skated the least per 60 among the grouping. The creative playmaker includes those around him very effectively – even if he isn’t generating individual scoring chances off his own blade.


Second least ice time was courtesy of Cam Atkinson who generated 10.15 scoring chance shots per 60 – only trailing John Tavares (11.31) among point leaders.


I’m also very curious as to who Johnny Gaudreau hit with his lone marker, and Artemi Panarin with two hits.


Using a summary table in this fashion will give you the ability to judge players among their peers and recognize anomalies in production at a fairly rapid pace. The key is to determine the categories which could change among different user needs. Generally, I would include individual and on-ice shooting percentages and all individual shot attempt metrics, Corsi, SCF – Scoring Chances attempts, and HDCF – high danger scoring chance attempts. Natural Stat Trick  doesn’t contain expected goals, but xG and actual goals scored can be included for even more depth.

Gus Katsaros
Gus Katsaros is the Pro Scouting Coordinator with McKeen’s Hockey, publishers of industry leading scouting and fantasy guide, the McKeen’s Annual Hockey Pool Yearbook. He also contributes to popular blog MapleLeafsHotStove.com ... he can be followed on Twitter @KatsHockey