Who are the best defensive players in the AHL? According to the process that I’ll explain momentarily, it might be someone like Chris Breen of the Providence Bruins and Brian Strait of the Binghamton Devils on defense, and Colin Campbell of the Grand Rapids Griffins, Cole Cassels of the Utica Comets, and Zach Sill of the Hershey Bears up front. Or, they might just be weak offensive players who were lucky enough to play on strong defensive teams. So, let’s back up and let me explain the approach. In my early years, when others were working on more fruitful endeavours like the development and analysis of shot-based statistics like Corsi, catch-all statistics like GVT, or shot quality work like expected goals, I was trying to develop a defensive statistic. At first, I looked at the plus/minus statistic, under the mistaken belief that if I could adjust it for the various factors that impact it (which was my first co-publication, with Iain Fyffe, in the Hockey Research Journal in 2001), and then removed the offensive component, then I would be left with the defensive component. Sadly, I learned that the best defensive players allow the most goals, because they’re up there against top opponents, while the weakest defensive players allow very few, because they’re only out there against the fourth lines, and tasked with playing low-event hockey. For example, in Ken Daneyko’s prime, he was -22 with the New Jersey Devils in 1988-89, while the defensively suspect Tom Kurvers was +11. Now that the NHL has this information online, we know that Daneyko was on for 74 goals against at even strength that season, and Kurvers was on for 71. So, essentially any use of plus/minus to measure defensive play is a dead end. Iain proposed another way to measure a player’s defensive play. He figured that every player was on the ice for a reason, and if that opportunity couldn’t be explained by his scoring (or something else that was measurable), then it must have been because of his defensive play, or some other intangible. That formed the basis of the defensive component of his own catch-all statistic, Point Allocations. So to answer today’s question, let’s apply Iain’s ideas to the AHL. I’ve got all the player data from 2005-06 to 2016-17 handy, so here’s what I did. 1. First, I figured out how many goals were prevented, league-wide. Under the assumption that offense and defense are equally important, the number of goals prevented is equal to the number of goals scored. Bear in mind, this isn’t an actual count of goals prevented (which would be a highly subjective exercise anyway), but rather a “thing” that we’re going to call goals prevented. 2. Then, I figured out how many goals were prevented by each team. Assuming that the opportunity to score is equal in all games, that means that this can be calculated by adding together the league average goals scored and goals prevented (which is just double goals scored), and then subtracting the actual goals that a team allowed. In theory, this could result in a negative number, but in practice it should result in a distribution that is exactly the same as goals scored. 3. Then, I set aside the goals that were prevented by the team’s goalies. But, just how many is that? First of all, the common perception is that goalies are responsible for roughly half a team’s goals prevented, or maybe a third. However, most of the models I’ve seen peg a goalie’s contributions somewhere between 20 and 30 percent. In this case, I arbitrarily decided to assume that a quarter of all goals prevented should be credited to the goalies. Now, that’s not the same on every team, that’s just the average. To break it down on a team-by-team basis, I modified the goals save above average (GSAA) statistic until it added up to a quarter of the goals prevented, league-wide. In its standard form, GSAA will add up to zero, because it multiplies the league-average save percentage by the number of shots a goalie faced, and subtracts that from the goalie’s actual saves. Through trial and error, I discovered that subtracting 0.0241 from the league-average save percentage before making that GSAA calculation generated results that added up to about a quarter of the league’s goals prevented. So, a goalie with a .899 save percentage will still prevent a few goals, but not very many. 4. Next, I figured out how much credit for those goals prevented should be awarded to defensemen versus forwards. I proceeded on the assumption that forwards and defensemen were equally valuable to a team. Therefore, the number of goals created plus the number of goals prevented for an average forward should equal that for an average defenseman, and vice versa. Goals created is a simple formula: add together a player’s goals plus his assists divided by the league-average number of assists per goal (so that assists are worth the same as a goal), and then divide by two. If you add up all of a team’s goals created, it should equal the team’s goals scored. Over this entire time span, a forward averaged 0.1882 goals created per game, and a defenseman averaged 0.1059. So, I reversed these numbers for the average number of goals prevented for each position. That is, I assumed defensemen prevent 0.1882 goals per game, and that forwards prevent 0.1059. In practise, I calculated these numbers on a season-by-season basis. Side Note: There is the argument that defensemen are more valuable than forwards, because they play more minutes. So, you could add up all the goals created by forwards and assume that an equal number of goals were prevented among all defensemen. The results will be 50 percent higher, because those goals were created by three forwards and the equal number of goals prevented are being dividing up among two defensemen. That will make defensemen far more valuable than forwards. While I don’t necessarily disagree with that assessment, I opted for the simpler approach this time around. 5. Ok, so now I finally assigned a team’s goals prevented to its forwards and to its defensemen. Here, I did so on an equal basis, with the exception that forwards and defensemen were being weighted differently, as computed in step four. For strong defensive teams, like the 2015-16 Albany River Rats, or the Wilkes-Barre/Scranton Penguins from 2010-11 through 2013-14, that meant multiplying the given average (0.1882 or 0.1059) by just under 1.2, while it got as low as 0.65 for weak defensive teams, like the old Binghamton Senators, or the 2007-08 Lake Erie Monsters. Side Note: For fun, I used this to figure out which players were frequently on particularly good or bad defensive teams. All I did was add up a player’s goals prevented over the entire time span (2005-06 to 2016-17), and divided it by their games played to create a “base team index” that might become useful later. At the very least, it indicates whether a player has played on particular strong or weak defensive teams, which is useful information in and of itself. For example, defensemen Ryan Lannon and Aaron MacKenzie often found themselves on good defensive teams, as did forwards like Matt Mangene and Nate Thompson. They were either really lucky, or doing something right. On the flip side, scoring-line forward Derek Grant and enforcer Darren Kramer were on some awful defensive teams, as was defenseman Ryan Murphy — that’s either bad luck or bad defense. 6. Ok, so the team’s goals prevented have been assigned, but on an entirely equal basis. Ideally, I’d like to see the same spread in goals prevented that we see in goals created. For example, strong offensive defensemen like T.J. Brennan and Brendan Montour average up to 0.3 goals created per game, while stay-at-home defensemen like Nathan McIver are below 0.03. I’d like to create that same spread in terms of goals prevented, except scaled up to reflect an average of 0.1882 instead of 0.1059. So, the range would be between 0.05 and 0.47. To do that, I assigned more of a team’s goals prevented to the shutdown defensemen, and fewer to the enforcers and/or one-way offensive defensemen. So, I divided a player’s goals created relative to the rest of the team’s players at the same position. Again, this goes back to Iain’s premise that everyone is in the lineup for a reason, so if they’re not there to score, then they were probably there to defend. So, rather than assigning 0.1882 per game for defensemen and 0.1059 for forwards (or whatever the averages were that season), I divided that number by the result I achieved here. This actually worked out nicely, because the average results varied from a low of around a third, to a high of three, which meant that it would make roughly the six-spread distribution I wanted. Oh, and two more details. I also did something similar for penalty minutes, just to filter out those who were in the lineup as enforcers. And, for those who didn’t play very many games, I regressed the results towards 1.0, up to 200 games (which was arbitrarily chosen). For example, if someone played 50 games and had twice as many goals created as expected, his end result of 2.0 was regressed down to 1.25. That’s 50 games at 2.0, and 150 games at 1.0. In terms of all-time results, the leaders among defensemen were from 2005-06 through 2016-17 were: Joey Mormina, 154.8 goals prevented in 670 games with an average team weight of 0.179 (average is 0.1882) Ryan Lannon, 146.9 in 318, with 0.219 Andrew Campbell, 142.0 in 595, with 0.164 Joe Piskula, 140.6 in 576, with 0.170 Brian Sopitz, 138.1 in 294, with 0.190 Maxime Fortunus, 133.2 in 803, with 0.176 Chris Breen, 131.2 in 400, with 0.175 Breen Palin, 127.1 in 367, with 0.205 Corbin McPherson, 112.7 in 287, with 0.185 Jaime Sifers, 110.9 in 544, with 0.183 Up front, the leaders were: Andrew Joudrey, 83.9 in 492, with 0.103 (average is 0.1059) Rod Pelley, 77.7 in 491, with 0.104 Zach Sill, 76.2 in 407, with 0.116 Mike Keane, 72.2 in 365, with 0.105 Brett Sutter, 72.0 in 678, with 0.096 Francis Wathier, 71.1 in 577, with 0.100 Harrison Reed, 70.9 in 208, with 0.092 Warren Peters, 68.8 in 578, with 0.106 Ryan Garlock, 68.1 in 321, with 0.096 Carter Bancks, 68.0 in 426, with 0.103 To arrive at the five players I listed at the top of the piece, I just computed this on a per-game basis, and chose the five top active AHL players. 7. (Future work) Ok, so the most glaring problem is that this system just rewards players for failing to score, and for being on great defensive teams. Sadly, it’s impossible to tell one-way defensemen and two-way defensemen apart using just the basic stats available in the AHL. Likewise, it’s impossible to separate the shutdown defensemen from third-pairing options who simply can’t score. In terms of traditional stats, both sets of players look identical. In the NHL, for example, Marc-Edouard Vlasic has 91 points and 358 shots in 212 games from 2014-15 to 2016-17, and Trevor Daley has 85 points and 327 shots in 206 games. In terms of higher-scoring defensemen, Duncan Keith has 141 points and 484 shots in 227 games over this time span, and Kevin Shattenkirk has 144 points and 476 shots in 208 games. In both instances, we subjectively know that one player is far superior defensively than the other, but there is just no way to establish that using basic stats. So what I need to do is to make a second pass through this data, and try to get a distribution of goals prevented among the different types of players. That is, even among the higher-scoring players, there needs to be a wider distribution of goals prevented. Yes, their average goals prevented per game should still be lower than the average among lower-scoring players, but the distribution should allow for lots of overlap. Having done this first pass, this could be achieved by simply widening the distribution among each tier of players. Furthermore, there are a ridiculous number of reasonable but completely unproven assumptions throughout this process, not to mention a number of reasonable but completely arbitrarily chosen numbers. Each step along the way can be improved by proving these assumptions, and calculating the correct numbers. But, what I have done is enough for the first pass to kill time on a snowy Saturday, so I’ll just leave this here for now, pause to reflect on the next step, and save the hard work for later. |

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