Once again, it is time to update my draft model that I use in creating my rankings for the NHL Entry Draft. The model consists of using historical rates of return for players based on draft position and utilizing statistical measurements that I have developed. The intent of the model is to find where scouts and stats collide to get the best of both worlds.
DRAFT VALUE FOR D-MEN BY DRAFT POSITION
To determine the draft value for defensemen by draft position, I utilize the career average time on ice (ATOI) as my measurement. While there are many different ways to measure the quality of a defenseman, time on ice is a classic way of rating their value. On the whole, the best defensemen are also the most trusted and therefore end up playing the most minutes for their team.
The first step is to group the defensemen by finding where they have similar rates of return based on ATOI. For my model, it utilizes defenseman drafted between 1998 and 2014 and the data precedes the start of the 2021-22 season. Using this method, I divide the 224 draft positions into 11 groupings. Here are the results for games played for these groups entering the 2021-22 season:
Here are the results for ATOI for these same groupings:
DRAFT VALUE FOR D-MEN BY ADJUSTED P/GP
In determining the draft value for defenseman via a statistical measurement, I utilize an adjusted point per game measurement. The reason for utilizing a point per game measurement, despite a weak correlation between junior scoring and NHL scoring for defenseman, is based off the research of Rhys Jessop. His research showcased that CHL defensemen who scored at a rate of 0.55 P/G or better in their draft season turn out to be much more likely at becoming an NHL defenseman. I took this research a step further by adjusting the P/G measurement based on the fact that power play scoring does not carry over to the NHL at the same rate as non-power play scoring. This was determined by looking at the career NHL season for CHL defensemen drafted between 1998 and 2014 that have played at least 250 NHL games. Overall, the power play P/GP for these defensemen in their career NHL season is 49% of their power play P/GP production in their draft year. Meanwhile, non-power play scoring, which includes even strength and shorthanded production, is 109% of their production in their draft year. Ultimately, the adjusted P/GP measurement values a power play point at 45% in relation to an even strength or shorthanded point.
Similar to my method of grouping by draft position, I once again group the defensemen by finding where there are similar rates of return based on ATOI. Ultimately, I divide the adjusted P/GP into 8 groupings. Here are the results for games played for these groups:
Here are the results for ATOI for the same groupings:
COMBINING SCOUTING AND STATS (D-MEN)
As I like to repeat over and over again, this is all about finding where scouting and stats collide and that both the eye test and the actual production are both important when evaluating talent. Simply relying just on scouting and doing the eye test for evaluating talent leads to poorer results. For example, there were 38 CHL defensemen drafted in the 10-19 range between 1998 and 2014. If we sort the defenseman by adjusted P/GP and split the two groups in half, we end up with 19 defenseman that have an adjusted P/GP that range between 0.49-1.20 and 19 defenseman that have an adjusted P/GP between 0.15-0.48. If the eye test was working, one would expect the two samples to have similar amounts in games played and average time on ice. They do not. On average, a defenseman selected 10-19 overall with an adjusted P/GP of 0.49 or greater plays 35% more games (419 vs 310) and 13% more minutes per game (18:43 vs 16:31) than those that have an adjusted P/GP less than 0.49.
DRAFT VALUE FOR FORWARDS BY DRAFT POSITION
To determine the draft value for forwards by draft position, I utilize games played and career average points per game (P/GP) as my measurement. As with defensemen, the first step is to group the forwards by finding where there are similar rates of return. Using this method, the 224 draft positions were divided into 10 groupings. These are the results for games played for these groups when entering the 2021-22 season:
Here are the results for P/GP for the same groupings:
DRAFT VALUE FOR FORWARDS USING NHLP
In determining the draft value for forwards by a statistical measurement, my point per game prediction formula called NHLP is utilized. A forward’s NHLP is their estimated career best point per game season in the NHL. The formula is derived from looking at the correlation of a CHL forward’s production (including playoffs) in their first draft eligible season and their career season in the NHL. For this, I looked at CHL forwards, drafted between 1998 and 2014, that went on to play 250 or more games in the NHL. The three factors utilized from a CHL forward’s draft season are:
- Points per game, separated between non-power play points (even strength and short handed) and power play points. This is the most important factor in determining a forward’s future NHL production.
- % contribution to team scoring, separated between non-power play and power play points.
For CHL forwards that play 250 or more NHL games, about 40% of their even strength and short handed production can be explained by their production in their first eligible draft season. For power play scoring it is 36%.
Even Strength and Short Handed Pts/GP: 1.061 + (0.270 X Non-PP Pts/GP) + (0.107 X PP Pts/GP) + (0.266 X Non-PP Contribution %) + (0.046 X PP Contribution %) – (0.046 X Age on Sep 15 of Draft Year)
Power Play Pts/GP: 1.152 + (0.220 X Non-PP Pts/GP) + (0.226 X PP Pts/GP) – (0.188 X Non-PP Contribution %) + (0.022 X PP Contribution %) – (0.063 X Age on Sep 15 of Draft Year)
For example, Matthew Savoie has 62 even strength/short handed points and 36 PP points in 73 games and has been involved in 24% of his team’s non-PP scoring and 43% of his team’s PP scoring. He will also be 18.7 years of age on Sep 15 of this year. This equates to an NHLP of 0.80 (0.56 for his non-PP scoring and 0.24 for his PP scoring). Therefore, based on his production so far in this draft season, Matthew Savoie is projected to have a career best season of 66 points.
Using this method, the NHLP of these CHL forwards can be divided into 9 groupings (there is actually a 10th grouping I did not include, which is for those individuals with a NHLP above 1.35. This group includes only two players named Crosby and McDavid). Here are the results for games played for the 9 groups:
For career average Pts/GP for the same groupings, these are the results:
Going back to the Matthew Savoie example, he had a NHLP of 0.80, which means he is in the 0.75-0.84 range. Historically, 42% of CHL forwards that fall in this range have played 300 or more NHL games while having a Pts/GP of at least 0.40. Based purely on his junior production in his draft season, Savoie is more likely to fail to live up to being a long time top nine forward in the NHL. It just goes to show you how difficult the jump is to make it to the pros.
COMBINING SCOUTING AND STATS (FORWARDS)
As someone who approaches the draft from a more analytical standpoint, I have heard the phrase time and time again that “it is more than just about the numbers, it’s about translatable skills.” That is very true, but it is also about the numbers and there is still a large presence in the scouting community that puts too much stock in their ability to judge talent. This is where a statistical measurement, like NHLP, can bridge the gap. For example, during the period of 1998 to 2014, 42 CHL forwards were drafted between 8th to 14th overall and of those 42 forwards, 10 of them (24%) had a NHLP of 0.85 or greater. Of those 10 forwards, 7 of them (70%) had a career average Pts/GP of 0.60 or greater. Meanwhile, of the remaining 32 forwards with a NHLP below 0.85 (76%), only 3 of those 32 forwards (9%) have a career average Pts/GP of 0.60 or greater. This is why stats matter because clearly the eye test has been failing scouts for a long time. Bridging the gap and utilizing additional analytical measurements such as NHLP can make a world of difference in making smarter decisions. Otherwise, you will continue to make the same errors over and over again. In the words of Carl Sagan, “you have to know the past to understand the present.”
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