Winning the QMJHL Draft with Advanced Predictive Modeling

Can a numbers-based approach outdraft QMJHL teams? I spent the last year trying to build an advanced predictive model to see if this was possible. As a test, I did a re-draft of the 2013 draft, since the majority of those players finished their QMJHL careers this season. Using exclusively information that was available before the draft and without taking into consideration any subjective factors (scouting, interviews, team needs, etc.), my model outdrafted every single team in the 2013 draft in terms of drafted player value (APV). While APV is not a flawless metric, it does correlate very highly with winning games and has successfully predicted the previous 10 President’s Cup winners. There is certainly some luck involved and I only tested one draft, but my results validate that there is value in looking at numbers in depth to help make draft decisions.


My predictive model was inspired by two NHL-based models. The Prospect Cohort Success (PCS) model was developed by Josh Weissbock and Cam Lawrence and, in my opinion, is the best hockey prospect evaluation model ever built. In fact, the model was so good that within a few months, both Weissbock and Lawrence were hired by the Florida Panthers, and the model was taken down from public view. The other model, called DEV, was created by Zac Urback, who now works for the Colorado Avalanche, and Hayden Speak. My learnings from their work was invaluable in helping me construct my model.

My model is based on historic data from the draft years of players from 2004 to present. Combining that data with my created metric called APV, I programmed a predictive model using a professional analytics platform. The model itself is an ensemble of regression and classification decisions trees (such as random forest variants). These algorithms all built upon themselves to optimize the output. So my “model” is actually thousands of different models working together.

Re-draft Results

2013 Draft - Actual vs. Expected

To test my model, I re-drafted using a simple approach:

  • Always draft the top rated player available, regardless of position
  • Only draft American players in the last 2 rounds, since they’ve historically been low probability draft choices

I had expected my predictive model to perform well, but I didn’t expect such an overwhelmingly positive result. On average, my model drafted twice the expected APV given a team’s picks. I measured the expected APV of a draft pick by calculating the average APV for that pick from 2004 to 2012, and then building a logarithmic equation that best fit those numbers.

My model was especially good at drafting under-valued players. The following players are the main reasons why my model outdrafted every team:

  • Daniel Sprong rated as the best player (was drafted 16th)
  • Thomas Chabot rated as the 6th best player (was drafted 22nd)
  • Mathieu Joseph rated as the 9th best player (was drafted 51st)
  • Samuel Laberge rated as the 13th best player (was drafted 95th)
  • Alex Barré-Boulet rated as the 19th best player (was drafted 102nd)
  • Étienne Montpetit rated as the 40th best player (was drafted 137th)
  • Tyler Boland was rated as the 106th best player (was drafted 180th)

My model certainly had its fair share of players who didn’t pan out (such as Alexandre Jacob and Marc-André Gauvreau), but still proved very effective at finding high potential players given their underlying numbers.

Player Career Projections

My predictive model produces more than simply a player’s projected APV. Here’s an example of all the information that is produced on a player:

Daniel Sprong

  • Predicted career APV: 1336
    • His actual career APV was 1280.
  • Volatility Index: 55
    • This measures the statistical confidence in the projection, the lower the score, the “safer” the projection. Score of 55 is a slightly above average risk factor.
  • Predicted APV per season:
    • Season 1: 243
    • Season 2: 336
    • Season 3: 378
    • Season 4: 379
  • Projected most probable player role per season:
    • Season 1: Top 6 forward (55%), bottom 6 forward (45%)
    • Season 2: Top 6 forward (91%), bottom 6 forward (9%)
    • Season 3: Top 6 forward (71%), not playing in QMJHL (17%)
    • Season 4: Top 6 forward (69%), not playing in QMJHL (27%)
      • The model correctly predicted that Sprong would spend parts of his 3rd and 4th seasons in the NHL.
  • Projected comparable players:
    • 76% similarity with Benjamin Breault
    • 64% similarity with Brandon Hynes
    • 60% similarity with Chris Doyle
    • 57% similarity with Guillaume Asselin
    • 55% similarity with Brad Marchand

Overall, this assessment that is based exclusively on quantifiable data ended up being quite accurate of how Sprong’s career in the Q panned out.

Team by Team Re-drafts

2013 Draft - AB2013 Draft - BB2013 Draft - BC2013 Draft - CB2013 Draft - CH2013 Draft - CHI2013 Draft - DR2013 Draft - GA2013 Draft - HF2013 Draft - MO2013 Draft - QU2013 Draft - RI2013 Draft - RO2013 Draft - SH2013 Draft - SHE2013 Draft - SJ2013 Draft - VD2013 Draft - VI



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Posted in APV Analysis, Draft Analysis

Introducing APV – A Single-number Player Evaluation Metric

The ultimate goal of hockey analytics is to win as many games as possible. There are many factors that influence the results of a hockey game given the sheer amount of almost random events that take place every single shift. And while some factors are intangible, such as leadership, much of what happens on the ice can be quantified. Single-number metrics tend to be effective ways to measure performance because they provide an instant comparison of all players and can be easily understood.

Actual Player Value (APV)

The goal of my project was to create a single-number metric that evaluates forwards, defencemen, and goalies in a comparable manner. I also wanted to make sure that this metric had a strong correlation to winning hockey games. I called my new metric Actual Player Value (APV) and it measures the value of the regular season performance of every player in the QMJHL. The APV scores go from 0 (negligible positive value) to close to 1000, with the league average being 100. Any player with a score above 100 is generally considered “above average”, and less than 100 “below average”.

MVP Nominees 2016-2017

This season for example, the skater with the highest APV was Samuel Girard at 680 (see table). He was nominated for league MVP, but Vitalii Abramov ultimately won it. All three nominees had very high APV scores and were all certainly deserving of the nomination. The reason that Girard’s APV is higher than Abramov or Roy is that, as a defenceman, he plays a larger share of his team’s ice time, and therefore brings more value to a team. Likewise, goaltenders can potentially have very high APV scores because they can significant impact a game.

Creating a Single-number Metric

At the NHL level, many have tried to create a “catch-all” metric. The gold standard for a catch-all metric is baseball’s WAR (wins above replacement). It’s an all-inclusive stat that can be used to compare players of all positions. However hockey is a far more fluid game than baseball, so no such perfect metric exist. Two of the best metrics that have been created are Tom Awad’s GVT (goals versus threshold) and Justin Kubatko’s Point Shares. Unfortunately, both use time-on-ice stats, which aren’t publicly tracked by the QMJHL. The player stats that are published in the QMJHL are fairly basic, so I only focused my work on the following stats: games played, goals, assists, penalty minutes, goals against average, and save percentage.

Calculating the APV Formulas

To create the formulas for my APV metric, I treated this as an optimization problem. I collected all regular season player stats from the past 10 QMJHL seasons (2007-2008 to 2016-2017), as well as the standing points for every team in the past 10 seasons. For the player stats mentioned above, I calculated the optimized weights that generated the highest correlation with team points.

Correlational Coefficients

The chart above shows the coefficients of correlation between a team’s total APV score and their points in a given season. As is often the case when quantifying players by position, forwards are the easiest ones to evaluate, followed by goaltenders, then defencemen. When added up together, a team’s total APV has an r-squared of 0.892, which means that the APV score can explain about 89% of the variance in a team’s points. With such as high r-squared value, the total team APV can also serve as a predictor of playoff success.

President's Cup Winners APV

The team with the highest APV has actually won the President’s Cup in all 10 years for which I’ve collected data. This metric could end up having a higher predictive value than my SRS team ranking system, at least when it comes to predicting the President’s Cup champion. However, APV is designed to be used as a player evaluation metric, not a team metric.

The breakdown by position gives an interesting look in how those championships teams were built. For example, the 2010 Moncton Wildcats had incredible goaltending and defencemen, while the 2013 Halifax Mooseheads where an offensive machine led by elite-level forwards.

Limitations of APV

Like all single-number metrics, APV has some limitations. It is most effective at evaluating forwards, although it is slightly biased against power forwards who take a lot of penalties, such as Jeffrey Viel or Boko Imama. It is fairly good at evaluating goaltenders, but those who play behind strong defensive teams will have their stats inflated. Finally, it is least effective at evaluating defencemen who don’t produce many points, given that there is no stat that directly measures “defensive ability”. As long as these limitations and the underlying assumptions behind the metric are understood, APV can be used as a simple yet effective way of measuring every single player in the league. However, for important player decisions, all available sources information should be taken into consideration to make the best decision possible.

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Posted in APV Analysis

The Relative Value of Drafting Left-shooting vs. Right-shooting Defencemen in the Q

All else equal, defencemen in the QMJHL who shoot right are more valuable than those who shoot left. I looked at every defenceman who was drafted in the Q from 2004 to 2012, and there is a tangible difference between left-shooting and right-shooting defencemen.

Right-handed defencemen are harder to find given that there are less of them available to draft. Only 35% of drafted defencemen shoot right, and as a result, only roughly 35% of defencemen in any given QMJHL season shoot right. Right-shooting defencemen also tend to score more goals and produce more assists. Anecdotal research suggests that right-shooting defencemen, given how rare they are, will be offered more powerplay opportunities, which might help explain their higher offensive output. As well, right-shooting defencemen take less penalty minutes on average than their left-shooting counterparts.

Left vs Right Shot

Despite all of these factors in favour of right-shooting defencemen being more valuable, they’re not drafted as early. The average draft pick at which a right-shot defenceman is selected is 118.9, while the average of a left-shot defenceman is 116.5.

In an article by the brilliant people over at titled “Quantifying The Importance of Handedness”, they concluded that NHL defencemen who play on their strong side are notably more effective than those who play on their off-side. Pairings that feature two defencemen who are on their strong sides are clearly more effective at both ends of the ice. This research could help explain the growing trend in the NHL to balance defencemen pairings. For example, coach Mike Babcock is a strong believer in balanced pairings. He famously selected players like Jake Muzzin and Jay Bouwmeester for Team Canada over Kris Letang and P.K. Subban due to shot-handedness, but it’s hard to argue with the results he’s had at the two previous Olympic tournaments.

In summary:

  • Right-shot defencemen are rarer
  • Right-shot defencemen score more goals
  • Right-shot defencemen produce more assists
  • Right-shot defencemen take less penalty minutes
  • Right-shot defencemen are drafted later
  • Defencemen who play on their strong side perform better

Given all of these factors, teams in the QMJHL should definitely be emphasizing balanced defencemen pairings and drafting right-shooting defencemen. While there are obviously numerous considerations when drafting a player, if two defencemen are valued similarly, the numbers definitely suggest drafting the one who shoots right. I would love to hear from coaches who’ve played around with this topic to see if their personal experience matches or contradicts my findings.

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Posted in Draft Analysis, Strategy Analysis

Assessing the Final Round of the 2017 Playoffs

The final round of the QMJHL playoffs begins tonight with the top seeded Saint John Sea Dogs facing off against the Blainville-Boisbriand Armada. Saint John made it to the finals by beating Chicoutimi in a highly-contested 6 game series, while Blainville-Boisbriand made it by handily beating Charlottetown in 5 games. The Sea Dogs finished 1st in the regular season as well as 1st in my SRS rankings. On the other hand, the Armada finished 5th in the regular season and 5th in my SRS rankings. Despite this gap, it should be a very close final as Blainville-Boisbriand has been performing at a very high level since mid-way through their 2nd round series.

Building a Championship Team

Before analyzing the final matchup, let’s take a look at how these two finalists were constructed. In terms of player acquisition, both the Sea Dogs and the Armada are built very similarly. As seen in the table below, the cores of their teams are made up of players that they drafted and developed (63% for BLB, 60% for SJ). They also added a few big pieces at the deadline (Dubois and Barré-Boulet for BLB, Booth, Bourque and Gauthier for SJ) to help push them over the finish line.

Team Composition 2017

In the QMJHL, the foundation of a championship team is built on draft day. Whether those draft picks become contributing players or assets in trades, a few strong drafts in a row will create a window to win the President’s Cup. The Rouyn-Noranda Huskies, the reigning President’s Cup winners, were the most homegrown team this year with an impressive 79% of players on their roster having been drafted by them. This year’s finalists were 5th (BLB) and 7th (SJ) respectively in terms of homegrown players. Also near the top of that list are two of the most promising young teams in the league, Halifax and Baie-Comeau, who should be championship contenders in the near future.

Saint John Sea Dogs vs. Blainville-Boisbriand Armada

4th Round Series

As expected, the Sea Dogs have performed at a dominant level throughout these 2017 playoffs. They swept their first two series against Rimouski and Val-d’Or, and defeated Chicoutimi in 6 games. They finished 1st in the regular season and my SRS ranking system had picked them to win the President’s Cup. They have depth and high-end talent across the team, and they go into this final with home ice advantage.

Their opponent, the Armada, swept their 1st round series against Drummondville, overcame a 3-1 series deficit to beat Bathurst in 7 games, then beat Charlottetown in 5 games. Having finished only 5th in my SRS rankings, they were predicted to lose against both Bathurst and Charlottetown. However, there is clearly a quality to this team that is not being picked up by my SRS system. I found two possible reasons for this:

  1. The SRS system favours teams that win by many goals; Blainville-Boisbriand has won numerous 1-goal games this year due to their defensive system and their star goaltender Montembeault
  2. Barré-Boulet, Teasdale and Mongo have all increased their scoring output significantly compared to the regular season; Barré-Boulet is leading the playoffs in scoring with 29 points in 16 games

Considering all of these factors, the final should be a hard-fought and thrilling series. The home ice advantage could prove to be a difference-maker in such a close matchup. I will stay true to my SRS methodology and predict Saint John to defeat Blainville-Boisbriand in 7 games. My SRS system has successfully predicted 8 of the last 9 President’s Cup winners, so we will see if that trend continues with a Saint John victory.

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Posted in Draft Analysis, SRS Analysis

Assessing the Third Round Matchups in the 2017 Playoffs

The 2017 QMJHL playoff semi-finals begin tonight as the quest for the President’s Cup has been narrowed down to four teams. The 2nd round featured two sweeps as well as two 7-game series. My 2nd round predictions went 2 for 4, incorrectly predicting the winners of both 7-game series. As a note for new readers, my playoff series assessment is based on my SRS rankings methodology, which is a combination of post-trade deadline goal differential and team strength of schedule.

Saint John Sea Dogs vs. Chicoutimi Saguenéens

3rd Round Series - Tier 1

The surprising Saguenéens will face their toughest test yet versus the dominant Sea Dogs, who have yet to lose a game in the playoffs. The SRS difference between the teams is fairly large and will be compounded by the difference in fatigue levels. Chicoutimi is coming off a grueling 7-game series that ended on Tuesday night, while Saint John has had the maximum amount of rest possible so far in these playoffs. However, Chicoutimi has a quality team that had the 4th best differential in pre-TD and post-TD SRS, which puts them just behind Saint John, who had the 3rd best differential. Overall, I expect the talented and well-balanced Sea Dogs to win the series in 5 or 6 games.

Charlottetown Islander vs. Blainville-Boisbriand Armada

3rd Round Series - Tier 2

The other semi-final will be a battle of the top offensive team in the regular season, the Islanders, versus the top defensive team in the regular season, the Armada. The SRS differential indicates that this should be a close series, but Charlottetown comes in with a slight advantage. Charlottetown has not lost a game in these playoffs and have been able to get a week of rest since their last series. On the other hand, Blainville-Boisbriand just finished a 7-game series against Bathurst, and will be starting the series on the road. A healthy Samuel Montembeault can steal a few games on his own, so look for this series to go 6 or 7 games, with the Islanders ultimately winning it.

A closer Look at the 2nd Round Results 

The “David vs Goliath Part 2″ matchup predictably ended in a 4-game sweep by Saint John. Val-d’Or goaltender Étienne Montpetit put up a historic 66 save performance in game 2, but it wasn’t enough to overcome the significant SRS differential between the two teams.

The “Fatigue Factor” matchup also ended up as a 4-game sweep, but was a highly physical series with several suspensions being given out. The fatigue factor combined with the SRS differential was too much to overcome for the young and hardworking Cape Breton team.

The two “Battle of Inches” matchups took 7 games to decide, with Chicoutimi actually winning it in overtime of game 7 after tying the game while shorthanded with only minutes left! Their opponent, Rouyn-Noranda, came into the series as a slight favourite from an SRS perspective, but their fatigue and injuries might have been the difference in such a closely-contested matchup.

The other series, featuring Bathurst and Blainville-Boisbriand, was a tale of two worlds. In games 2 to 4, Bathurst won 3 straight games in a dominant fashion, outscoring their opponents by 9 goals and their powerplay scoring on 55% of their opportunities. In games 5 to 7, Blainville-Boisbriand won 3 straight games, outscoring their opponents by 13 goals and their powerplay scoring on 50% of their opportunities. The SRS scores indicated that these were two teams of a similar talent level, and an entertaining 7-game series was the result.


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Posted in SRS Analysis

Should Goalies Play Back-to-back Games in the QMJHL?

A player performance management strategy that has come out of the NHL’s analytics movement is to avoid playing goalies two nights in a row whenever possible. The first popular article written on the topic was Eric Tulsky’s analysis that concluded that goalies should almost never play back-to-backs, with the difference between a tired and a rested goalie’s save percentage being 0.020. According to that study, a rested backup goalie was almost always a better option than a tired starting goalie. The significance of this result helped create a shift in goalie usage in the NHL, where teams are resting their goalies much more than in the past.

However, Tulsky’s analysis was limited to 2 seasons of data and was prone to sample bias. Similar studies where performed later, notably by Olivier Bouchard, and the results found a much smaller difference between tired and rested goalie. Bouchard found that the difference in save percentage over 7 seasons was 0.002, which has much different implications for player performance management strategy.

Analysis of QMJHL goalies

I decided to perform a similar type of analysis on QMJHL goalie data, although with a more vigorous process to avoid any biases or misleading results. To ensure that I had a large enough sample size, I captured goalie data from every regular season game from the 2010-2011 season to the 2016-2017 season. To avoid any effects caused by team fatigue, I filtered down my data to only include the 2nd games of back-to-backs. Finally, I only used data from goalies that had played at least 4 back-to-back games tired and 4 back-to-back games rested. At the end, I had 84 “goalie seasons” to compare the impact of playing tired versus rested.

Chart 1

The result: an average difference in save percentage of 0.002

This matches what Oliver Bouchard’s study of NHL goalies had found. It is interesting that despite the differences in age, skill level of shooters, and season length, the impact is essentially the same. Going back to the initial question, should goalies play back-to-back games in the Q? Based on the numbers, there isn’t much of an effect. The only real situation where this finding could be useful would be if a team has two very evenly talented goalies. For example, Charlottetown has had essentially two starting goalies this year:

  • Mark Grametbauer: 0.897 SV% in 40 games
  • Matthew Welsh: 0.899 SV% in 38 games

Given how closely those 2 goalies have performed this year, the numbers would support the goalies splitting back-to-backs. However, the fatigue effect is minimal so other considerations like recent form, injury proneness, and big game experience could be more important. Overall, teams should be expecting a slight decline in a goalie’s performance in the 2nd game of a back-to-back.

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Posted in Strategy Analysis

Assessing the Second Round Matchups in the 2017 Playoffs

The 2nd round of the 2017 QMJHL playoffs begins tomorrow and with it will come much more competitive matchups. The 1st round featured 5 sweeps and only one game 7, but the 2nd round should offer some thrilling series. My 1st round predictions went 7 for 8, with 6 series being predicted in the correct “competitiveness tier”. As a reminder, my playoff series assessment is based on my SRS rankings methodology, which is a combination of post-trade deadline goal differential and team strength of schedule.

Tier 1 – The David vs Goliath Part 2 Matchup

2nd Round Series - Tier 1This matchup represents a serious talent mismatch with the Sea Dogs expected to beat the Foreurs in 4 or 5. Étienne Montpetit had a remarkable series against Shawinigan where he boasted a 0.937 save percentage, but even he shouldn’t be enough to stop the President’s Cup favourites. This series represents the largest SRS differential so far in the 2017 playoffs.

Tier 2 – The Fatigue Factor Matchup 

2ndRound Series - Tier 2The Charlottetown Islanders go into this series as comfortable favourites over their tired Maritime division rivals. The Screaming Eagles was pushed to the absolute brink of elimination and only have one day of break before facing off against the 2nd most dominant team in the QMJHL. While Cape Breton should put up a good fight, expect the Islanders to beat them in 5 to 6 games. The SRS differential should be magnified by the difference in fatigue levels between the teams.

Tier 3 – The Battle of Inches Matchups

2nd Round Series - Tier 3These two matchups should be closely fought with very little distinguishing the teams. On paper, Rouyn-Noranda should be able to beat Chicoutimi. However, the Huskies just finished a grueling series and have numerous injuries while the Saguenéens have had a long rest and enter this series with plenty of momentum. That being said, I will stick by my SRS system and expect Rouyn-Noranda to win it in 6 or 7 games.

The final matchup of the 2nd round will be a battle of contrasting styles. Blainville-Boisbriand, lead by goaltender Samuel Montembeault, is a top defensive team on the league, where as Acadie-Bathurst is a top offensive team. Both are going into this series undefeated, with the Armada being the higher seed. However, the SRS rankings are in favour of the Titan, due to them playing in a tougher division and having a stronger goal differential. Ultimately, I expect the Titan to beat the Armada in 6 or 7 games.

A closer Look at the 1st Round Results 

All 4 of my “David vs Goliath” matchups ended in 4 game sweeps. Saint John, Charlottetown, Blainville-Boisbriand, and Acadie-Bathurst simply overpowered their opponents. The SRS differential within the matchups were all significant, which meant a serious mismatch in terms of team quality.

The 2 “Middle of the Road” matchups ended up being great series. As expected, Rouyn-Noranda was pushed hard by Halifax, but ultimately won in 6 games. The one series that I did not predict correctly was Shawinigan – Val-d’Or, where the Foreurs won in 6. While it was surprising to see Val-d’Or perform at that high of a level, Shawinigan’s downfall could certainly have been predicted by looking at the SRS numbers. They were only ranked 8th in SRS despite finishing 3rd in the standings, mostly due to a weak division. As well, Shawinigan had the 3rd worst differential in pre-TD and post-TD SRS in the league. This team had no momentum going into the playoffs and the results showed it.

The 2 “Home Ice Advantage” matchups were complete opposites. The Cape Breton – Gatineau series went to overtime in game 7, with the Screaming Eagles winning in front of their home crowd. However, Chicoutimi beat Victoriaville in 4 straight games. While they were predicted to win, their dominance while doing so surprised more than a few. Once again, the SRS numbers had given an indication that this result might happen. Chicoutimi had the 4th best differential in pre-TD and post-TD SRS, which means that they came into the playoffs with a lot of momentum and it helped carry them to the 2nd round.

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Posted in SRS Analysis

What’s at stake in the 2017 QMJHL draft lottery? A look at the relative value of a draft pick

The 2017 QMJHL draft lottery will be taking place on Thursday afternoon with a brand new draft format. For the first time, the five teams at the bottom of the standings will get an opportunity to win the 1st overall pick. While all participating teams obviously want to win, what kind of an impact does winning the draft lottery ultimately make? According to my analysis of relative draft pick value, the difference between the top 5 picks is significant.

The value of draft picks has been studied by numerous people at the NHL level, such as Eric Tusky’s work and Stephen Burtch’s work. However, it has never been studied at the QMJHL level. Building from the NHL analyses, I created my own relative draft picks values using regular season data from a 10-year period (2002 to 2011). To calculate a relative value for draft picks, I combined the expected probabilities of a draft pick playing 50, 100, or 150 games with the expected production of that pick. Given that point per game is the most reliable measure of production, I filtered out defencemen and goalies from this analysis. I also limited my draft pick values to the first 5 rounds (90 picks), given that my data is based on former league rules where first-time eligible players could only be drafted in those 5 rounds.

The results of this analysis revealed a large gap in the value of the first 5 picks. Assuming that the 1st overall pick is worth 100%, the 2nd pick is worth 80%, the 3rd pick is worth 70%, the 4th pick is worth 64%, and the 5th pick is worth 60%. It is interesting to note the difference in relative pick value in the 1st round compared to the subsequent rounds. The difference in value between the 1st pick and 18th pick is a massive 60%, while the difference between the 37th pick and the 90th pick is only 7%.

Draft Pick Value

The 2017 draft lottery odds are the following:

  • 18th – Moncton: 43%
  • 17th – Sherbrooke: 28%
  • 16th – Rimouski: 14%
  • 15th – Halifax: 10%
  • 14th – Val-d’Or: 5%

For Moncton in particular, losing the draft lottery would be a major detriment to their rebuilding efforts, by getting a player that projects to only make 80% or 70% of the performance impact that the 1st overall pick would. At the other end of the spectrum, Val-d’Or winning the lottery would nearly double the value of their pick, in addition to the team heading to the 2nd round of the playoffs! Regardless, it will be a potentially franchise-altering moment for whichever team wins the lottery on Thursday.

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Posted in Draft Analysis

Couturier (Bat) and Hulton (Cha) should have definitely been nominated for GM of the Year

Yesterday, the QMJHL announced the nominees for its various awards for the 2016 – 2017 season. While the player awards seem to follow the general consensus among media and fans, the two hockey personnel awards were highly disputed as usual. Unlike players and their personal stats, it can be difficult to quantify the work of coaches and general managers. That being said, there are certain ways to objectively measure their performance, and based on my SRS split-season analysis, Acadie-Bathurst Titan GM Sylvain Couturier and Charlottetown Islanders GM Jim Hulton definitely deserved to be nominated for GM of the Year.

Evaluating the work of a GM can be tough given that they perform so many behind the scenes tasks. One relatively straightforward way to compare GMs is to look at player transactions. For a yearly award that is based on a single season’s worth of work, draft picks are too future-based to be weighted heavily. Therefore, I will be looking at in-season trades to objectively evaluate who should have been nominated for GM of the Year in 2016 – 2017.

As discussed in my previous blog posts, my SRS analysis has proven to be an effective measure of team quality. In this case, I am looking to evaluate which GMs most improved their team’s SRS score during the year, so I will be dividing the season into pre-trade deadline and post-trade deadline. The table below shows every team’s SRS scores for both halves of the season, sorted by difference between the scores.

Trade Deadline SRS Table

Without a doubt, the most improved team following the trade deadline was the Acadie-Bathurst Titan. Sylvain Couturier’s team went into the deadline as the 8th best SRS team. They already had one of the top scoring teams in the league with a top 9 loaded with size and talent. However, they struggled defensively and went through a carousel of goaltenders in the first half. So Couturier went out and got 2 top pairing defencemen, Holwell and Malatesta, and a veteran number 1 goalie, Dumont-Bouchard. All 3 additions immediately helped improve the team and they had a very strong second half of the season where they ranked as the 3rd best SRS team. Couturier significantly improved his team while only making 3 key additions, which is a remarkable display of player acquisition efficiency.

Bathurst Trade Deadline

The second most improved team following the deadline was the Charlottetown Islanders. Jim Hulton’s team went into the deadline as the 4th best SRS team, and realizing that this was their year to go for it, they absolutely loaded up. A staggering 9 players were brought in to reinforce an already talented team. Combined with the return of star forward Daniel Sprong, the Islanders had a dominant second half and finished 2nd in SRS, barely behind the league leading Saint John Sea Dogs. Hulton’s approach was certainly different than Couturier’s, as he opted for making as many upgrades as possible in all areas of the team. But the results speak for themselves and heading into the playoffs, they are a top contender who will certainly challenge for the President’s Cup.

Charlottetown Trade Deadline

Given the above analysis, I believe that Sylvain Couturier and Jim Hulton should have definitely been nominated for GM of the Year, with Couturier ultimately winning the award due to his player acquisition efficiency. Agree or disagree? Feel free to let me know in the comments below or on Twitter.


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Posted in SRS Analysis, Trade Analysis

Assessing the First Round Matchups in the 2017 Playoffs

Historically, the first round of the QMJHL playoffs have featured some very lopsided results, along with the occasional upset. This year’s matchups, according to my SRS team rankings, all give the top seed a noted advantage. However, the differences between the top and bottom seeds can be divided into 3 tiers of competitiveness. For a refresher on my SRS rankings, check out my post on predicting the QMJHL playoff champion using SRS.

Tier 1 – The David vs Goliath Matchups

1st Round Series - Tier 1

With 16 of the 18 teams in the league making the playoffs, it is a given that there will be some talent mismatches in the first round. These 4 series should all be over in 4 or 5 games, with the SRS differential between the two teams all greater than 2.00. Saint John, Charlottetown and Bathurst have been the top 3 SRS teams since the trade deadline, and should comfortably win their respective series. Blainville-Boisbriand is also included in this tier, despite having a lower ranking than Rouyn-Noranda, due to having received the easiest opponent in the first round, Drummondville, who have been struggling badly since the trade deadline.

Baie-Comeau was particularly unlucky with their playoff matchup. The Drakkar have been playing very well since the trade deadline, good for 10th in my SRS rankings, but they’ve been matched up with the 2nd best SRS team in the league, Charlottetown. Had they finished with 1 less point, they would have been playing the Shawinigan Cataractes, which would have been a much closer series.

Tier 2 – The Middle of the Road Matchups

2nd Round Series - Tier 1

There are 2 matchups that should be more competitive than tier 1, but should still be relatively comfortable wins for the top seed with the SRS differential between the teams both between 1.00 and 1.50. Rouyn-Noranda has a very talented team once again this year, and should be able to beat Halifax in 5 to 6 games. However, Halifax will give them some tough games given that they are accustomed to playing high-end teams (almost 40% of their games this season were played against Saint John, Charlottetown, or Bathurst).

Shawinigan should also be able to beat Val-d’Or in 5 to 6 games, but the Cataractes have been struggling since the trade deadline. In the first half of the season, Shawinigan was actually 1st in my SRS rankings. They’ve underperformed in the 2nd half, but this remains a talented team that should be able to see off the Val-d’Or Foreurs, who finished 14th in my SRS rankings.

Tier 3 – The Home Ice Advantage Matchups  

1st Round Series - Tier 3

The two other series should be closely fought and provide some highly entertaining hockey, with the top seed’s home ice advantage potentially being the difference maker. The SRS differential between the two teams are both less than 1, indicating evenly-matched teams. Cape Breton, despite trading away their top player at the deadline, have been playing very well and finished 6th in my SRS rankings. Their opponent, Gatineau, has had a much better 2nd half of the season, and should be able to push the series to 6 or 7 games. That being said, expect Cape Breton to come out on top at the end of this potentially grueling series.

The final matchup will be between the 8th seeded Chicoutimi and the 9th seeded Victoriaville. As expected, this is the closest matchup from an SRS perspective. Chicoutimi added a lot of talent at the trade deadline, and that can partially explain their much stronger 2nd half of the season. Victoriaville has consistently been 9th in my SRS rankings, both pre and post trade deadline, and should prove to be a tough matchup for Chicoutimi. But given the Saguenéens’ strong recent form and home ice advantage, they should be able to win the series in 6 or 7 games.

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