## Using Win Threshold to Aniticpate Next Season Part III

In Part I we explained what Win Threshold is, how it works, and which teams had the best Win Threshold (or which team’s offense is the best at scoring and defence is best at defending) last season. In Part II, we examined the Leafs offensive outputs based on shot percentages and shot totals, and factored in the new additions to the leafs in order to determine what kind of Goals For we can expect next season. Welcome to Part III, where we will discuss how our team is doing defensively, and specifically limiting shots.

First, I invite you to re-read Part I & Part II,  to re-familiarize yourself with this material, before we begin the analysis on the Leafs defensive system.

By the end of Part II, we calculated that the Leafs should be good for goal totals between 230-250, depending on potential injuries and the slight development of Nazem Kadri. That gave us the following table of output:

 Goals For Equation Win Threshold League Rank Conference Rank 230 (2542-230)/2542 0.9095 16th 8th 235 (2542-235)/2542 0.9075 14th 7th 240 (2542-240)/2542 0.9056 13th 6th 245 (2542-245)/2542 0.9036 12th 5th 250 (2542-250)/2542 0.9017 10th 4th

These projections, however, are based on the assumption that the Leafs will limit their opposition to exactly 2542 shots against again this year. This is chances of this are obviously extremely farfetched, and thus in this article we are going to try to determine what kind of shots to expect against our goalies this coming season.

Below is a distribution of the leaf’s shots against per game over the entire season. Obviously the shots fluctuate game to game based on the opposition, the offense generated, whether Lebda was dressed or not, etc. When averaging the Shots Against on a per game basis, however, a clear trend arises. Because the first few games the Leafs the year saw lower-than-average shots against, the Shots/GP is constantly going up. One would expect it to level off at about the half way mark of the season, however—this never occurred, the reason being, that our shot totals were increasing as the season wore on.

On Februrary 9th, 2011, Francois Beauchemin was traded from the Leafs back to Anaheim, in exchange for Joffery Lupul and Jake Gardiner. While this is an excellent haul from an asset aspect, Beauchemin was a defensive stalwart on the Leafs blueline that is extremely hard to replace.

In addition to the trading of Beauchemin, the Leafs also lost Kris Versteeg for draft picks, John Mitchell, and Tomas Kaberle. None of these trades, except for Lupul from Anaheim, gave the Leafs any sort of replacement into the lineup. Thus the defensive prowess of some of these players could not be replaced, evidenced by the fact that while the leafs finished the season with an average shots against of 31.0/gp, from October 9th – Februrary 8th they averaged 29.8 SA, whereas from Februrary 10 – April 9 they averaged 33.2SA.

Below is the breakdown in table format:

 Date Game # SA Total Shots Against SA/G 07 Oct '10 1 28 28 28.0 09 Oct '10 2 18 46 23.0 13 Oct '10 3 25 71 23.7 15 Oct '10 4 24 95 23.8 18 Oct '10 5 20 115 23.0 21 Oct '10 6 32 147 24.5 23 Oct '10 7 40 187 26.7 26 Oct '10 8 22 209 26.1 28 Oct '10 9 32 241 26.8 30 Oct '10 10 24 265 26.5 02 Nov '10 11 24 289 26.3 03 Nov '10 12 30 319 26.6 06 Nov '10 13 31 350 26.9 09 Nov '10 14 25 375 26.8 10 Nov '10 15 28 403 26.9 13 Nov '10 16 32 435 27.2 16 Nov '10 17 34 469 27.6 18 Nov '10 18 30 499 27.7 20 Nov '10 19 39 538 28.3 22 Nov '10 20 22 560 28.0 26 Nov '10 21 28 588 28.0 27 Nov '10 22 28 616 28.0 30 Nov '10 23 39 655 28.5 02 Dec '10 24 19 674 28.1 04 Dec '10 25 27 701 28.0 06 Dec '10 26 36 737 28.3 08 Dec '10 27 26 763 28.3 09 Dec '10 28 28 791 28.3 11 Dec '10 29 23 814 28.1 14 Dec '10 30 24 838 27.9 16 Dec '10 31 33 871 28.1 18 Dec '10 32 29 900 28.1 20 Dec '10 33 23 923 28.0 26 Dec '10 34 30 953 28.0 28 Dec '10 35 27 980 28.0 30 Dec '10 36 32 1012 28.1 01 Jan '11 37 33 1045 28.2 03 Jan '11 38 33 1078 28.4 06 Jan '11 39 44 1122 28.8 07 Jan '11 40 44 1166 29.2 10 Jan '11 41 32 1198 29.2 11 Jan '11 42 42 1240 29.5 13 Jan '11 43 21 1261 29.3 15 Jan '11 44 33 1294 29.4 19 Jan '11 45 28 1322 29.4 20 Jan '11 46 28 1350 29.3 22 Jan '11 47 31 1381 29.4 24 Jan '11 48 28 1409 29.4 25 Jan '11 49 31 1440 29.4 01 Feb '11 50 33 1473 29.5 03 Feb '11 51 27 1500 29.4 05 Feb '11 52 43 1543 29.7 07 Feb '11 53 34 1577 29.8 08 Feb '11 54 34 1611 29.8 10 Feb '11 55 39 1650 30.0 12 Feb '11 56 39 1689 30.2 15 Feb '11 57 35 1724 30.2 16 Feb '11 58 24 1748 30.1 19 Feb '11 59 22 1770 30.0 22 Feb '11 60 29 1799 30.0 24 Feb '11 61 35 1834 30.1 26 Feb '11 62 40 1874 30.2 27 Feb '11 63 43 1917 30.4 02 Mar '11 64 29 1946 30.4 03 Mar '11 65 30 1976 30.4 05 Mar '11 66 25 2001 30.3 08 Mar '11 67 40 2041 30.5 10 Mar '11 68 33 2074 30.5 12 Mar '11 69 42 2116 30.7 14 Mar '11 70 36 2152 30.7 16 Mar '11 71 37 2189 30.8 17 Mar '11 72 26 2215 30.8 19 Mar '11 73 37 2252 30.8 22 Mar '11 74 29 2281 30.8 24 Mar '11 75 28 2309 30.8 26 Mar '11 76 29 2338 30.8 29 Mar '11 77 31 2369 30.8 31 Mar '11 78 38 2407 30.9 02 Apr '11 79 25 2432 30.8 05 Apr '11 80 41 2473 30.9 06 Apr '11 81 33 2506 30.9 09 Apr '11 82 34 2540 31.0

Looking at this breakdown, the increases in shots against after the trade deadline, and the knowledge that the Leafs were allowing 33 shots per game after the Beauchemin trade, one can expect that the leafs will continue to allow around 33 shots per game next season. This does not factor in any of the new additions, though, and is a pretty loose assumption. For this reason, we will look at some advanced statistics that will help us to determine the shots against we can expect.

In order to maintain consistency throughout or study, and based on the moves the Leafs have made, we can expect our opening roster to look like this:

CAPGEEK.COM CAP CALCULATOR

FORWARDS
Joffrey Lupul (\$4.250m) / Tim Connolly (\$4.750m) / Phil Kessel (\$5.400m)
Clarke MacArthur (\$3.250m) / Mikhail Grabovski (\$2.900m) / Nikolai Kulemin (\$2.350m)
Nazem Kadri (\$1.720m) / Matthew Lombardi (\$3.500m) / Colby Armstrong (\$3.000m)
Mike Brown (\$0.736m) / Tyler Bozak (\$1.500m) / Colton Orr (\$1.000m)
/ / Darryl Boyce (\$0.650m)
/ / Joey Crabb (\$0.650m)

DEFENSEMEN
Carl Gunnarsson (\$1.325m) / Dion Phaneuf (\$6.500m)
John-Michael Liles (\$4.200m) / Luke Schenn (\$3.500m)
Keith Aulie (\$0.733m) / Cody Franson (\$0.800m)
/ / Mike Komisarek (\$4.500m)

GOALTENDERS
James Reimer (\$1.800m) / Jonas Gustavsson (\$1.350m)

(these totals are compiled without the bonus cushion)
SALARY CAP: \$64,300,000; CAP PAYROLL: \$60,164,000; BONUSES: \$1,000,000
CAP SPACE (23-man roster): \$3,714,000

Based on this roster, we generate defensive stats for the following players:

EVEN STRENGTH:

 NAME POS GP TOI/60 REL+/- CorsiQoC CorsiQoT SVPCTA PDO SOGF/60 SOGA/60 PTAKE/60 PDRAW/60 Pdraw-Ptake/60 Joe Colborne LW 1 14.23 6.11 -0.32 -4.811 1000 1167 21.1 29.5 0 0 0 Mike Komisarek D 75 11.89 -0.4 0.05 0.43 912 1000 23.7 29.9 1.1 0.3 -0.8 Keith Aulie D 40 16.24 0.05 1.119 1.379 938 1024 22.5 33.6 0.9 0.3 -0.6 Luke Schenn D 82 18 0.11 0.392 0.732 915 1000 26.9 28.5 0.4 0.3 -0.1 Joey Crabb RW 48 11.29 -0.12 0.689 -2.452 919 1009 25.8 31.3 1.1 0.4 -0.7 Colton Orr RW 46 5.02 0.26 -0.885 -5.292 944 1021 15.6 26.5 3.4 0.5 -2.9 Dion Phaneuf D 66 18.62 -0.21 0.724 0.01 921 1004 25.9 29.2 0.7 0.5 -0.2 Mike Brown LW 50 8.38 0.22 -0.111 -5.454 935 1031 18.8 28.8 0.6 0.6 0 Carl Gunnarsson D 68 13.86 -0.17 0.19 0.187 917 1005 23.7 28.1 0.4 0.6 0.2 Nikolai Kulemin RW 82 13.35 1.16 0.464 6.9 917 1018 27.3 27.1 0.5 0.9 0.4 Tyler Bozak C 82 13.98 -1.6 0.293 -1.459 904 970 26.8 30.5 0.3 0.9 0.6 Phil Kessel LW 82 15.19 -1.01 0.28 -0.808 906 983 27.8 31.1 0.3 0.9 0.6 Darryl Boyce C 46 10.47 1.21 0.857 -2.019 920 1069 20.7 30 1.5 1 -0.5 Mikhail Grabovski C 81 14.73 1.49 0.465 5.144 917 1015 29.5 26.5 1.1 1 -0.1 Colby Armstrong RW 50 13.28 0.16 0.321 -3.22 905 1010 23.9 27.6 0.6 1.2 0.6 Clarke MacArthur LW 82 13.97 0.63 0.379 5.5 909 1005 27.7 27.1 0.5 1.2 0.7 Joffrey Lupul RW 54 12.53 -0.81 0.518 -2.858 917 1008 23.9 33.5 0.4 1.4 1 Nazem Kadri C 29 12.94 0.06 0.096 -1.879 941 1001 27.7 28.3 0.5 2.2 1.7 Tim Connolly C 68 11.74 -0.44 0.321 2.137 906 986 30.1 25.9 0.5 0.5 0 Cody Franson D 80 12.84 0.26 -0.673 -0.87 943 1021 28.5 29.2 0.8 0.1 -0.7 Matthew Lombardi C 2 11.43 -5.23 -0.546 0.34 938 938 18.4 39.4 0 0 0 John-Michael Liles D 76 16.93 0.33 0.484 0.361 898 986 28.1 25.4 0.3 0.8 0.5

PENALTY KILL:

 NAME POS GP TOI/60 REL+/- CorsiQoC CorsiQoT SVPCTA PDO SOGF/60 SOGA/60 Mike Komisarek D 75 1.51 -1.09 1.579 0.246 826 898 6.9 40.3 Clarke MacArthur LW 82 0.01 7.31 0.964 -4.616 0 0 0 0 Joe Colborne LW 1 0.07 26.28 5.041 13.812 0 0 0 0 Luke Schenn D 82 2.58 -1.26 1.116 -2.607 826 871 11.9 40.3 Nazem Kadri C 29 0.02 8.87 0.476 4.432 0 0 0 0 Darryl Boyce C 46 0.36 3.73 -6.615 -2.34 917 917 14.5 40 Joffrey Lupul RW 54 0 6.27 2.444 -6.807 0 0 0 0 Joey Crabb RW 48 0.82 0.56 2.166 -5.847 839 982 9.2 39.8 Tyler Bozak C 82 1.76 0.97 0.562 -2.101 864 909 8.7 44.8 Nikolai Kulemin RW 82 0.92 4.02 3.038 2.747 880 943 11.9 35 Keith Aulie D 40 2.02 1.06 -7.234 -0.365 855 855 8.2 43.8 Mikhail Grabovski C 81 0.99 6.1 0.217 1.871 917 969 13.4 32.8 Dion Phaneuf D 66 1.91 2.4 2.457 0.527 854 1031 6.6 38.9 Colton Orr RW 46 0.01 7.26 -1.035 -0.82 0 0 156.5 0 Colby Armstrong RW 50 1.85 -3.29 -0.816 -4.28 827 827 8.5 40.3 Phil Kessel LW 82 0.12 13.86 -4.235 -2.604 1000 1333 12.5 56.3 Carl Gunnarsson D 68 2.54 -3.35 -6.733 -0.143 818 854 9.4 40.6 Mike Brown LW 50 1.58 -0.82 -17.991 0.534 790 874 8.3 37.1 Cody Franson D 80 0.12 -9.12 0.932 11.473 500 0 0 12.9 Matthew Lombardi C 2 0.01 9.55 1.512 12.718 0 0 0 0 John-Michael Liles D 76 1.03 6.45 0.633 -0.083 902 944 17.5 28.2 Tim Connolly C 68 1.52 0.65 2.073 0.003 901 901 7 47.7

POWER PLAY:

 NAME POS GP TOI/60 REL+/- CorsiQoC CorsiQoT SVPCTA PDO SOGF/60 SOGA/60 Clarke MacArthur 8.3 16 82 3.55 0.183 -77.42 12.09 889 1.03 41.3 Mikhail Grabovski 7.3 84 81 3.42 0.074 -79.14 12.82 882 0.98 41.5 Joey Crabb 4.5 46 48 5.35 0.077 -82.55 16 750 1.5 31.4 Colby Armstrong 9.9 9 50 5.69 0.261 -70.859 4.76 1000 0 39.4 Mike Komisarek 0 8 75 6.32 -1.853 -82.543 33.33 0 0 41.1 Mike Brown 0 18 50 6.51 -0.302 -84.809 16.67 0 0 56.3 Dion Phaneuf 9.8 3 66 2.54 0.561 -74.688 12 930 0.73 37.7 Nazem Kadri 10.6 43 29 3.79 0.311 -77.739 7.41 813 2.45 40.9 Joe Colborne 13.7 32 1 5.08 -1.55 -81.266 0 1000 0 13.7 Phil Kessel 8.7 81 82 2.86 0.076 -78.717 13.04 913 0.83 37.3 Darryl Boyce 7.8 47 46 5.67 -0.471 -78.078 0 1000 0 19.4 Nikolai Kulemin 7.8 41 82 3.76 0.518 -83.437 10.29 875 1.11 43.7 Tyler Bozak 9.6 42 82 3.58 0.508 -78.485 11.39 881 1.3 36.4 Luke Schenn 7.3 2 82 5.39 -0.186 -71.585 6.38 909 0.73 32.3 Keith Aulie 6.4 59 40 6.37 -0.52 -83.584 0 1000 0 38.6 Joffrey Lupul 10 19 54 3.28 -0.032 -79.318 16 926 0.8 42 Carl Gunnarsson 7.5 36 68 5.1 -0.109 -73.842 10.53 917 0.68 34.7 Colton Orr 0 28 46 6.31 1.452 -81.032 0 0 0 30.5 Cody Franson 6.9 4 80 3.36 -0.165 -82.913 10.48 944 0.4 37.9 Matthew Lombardi 0 15 2 5.55 -0.114 -85.574 0 0 0 49.7 John-Michael Liles 6.5 4 76 2.19 0.325 -81.833 13.02 806 1.55 48.3 Tim Connolly 9.4 19 68 2.64 1.066 -79.065 13.66 800 2.36 46.9

In Part II we also assumed each Skater would play only 74 games—to account for injuries—except for Connolly who will only play 60, Lombardi who will play 22, Colborne who will play 24, Crabb who will play 64, and Komisarek who will play 48. We will maintain these numbers for consistency and realism’s sake.

Because of this stupid box that I have to type everything in, I am unable to properly paste the table that I want to. Thus, I will have to split the following calculations by situation, and then amalgamate them all at the end. It may be confusing, but I will try my best to explain the calculations I will be performing.

The actual mathematical representation of our calculations are below the explanation, and above the table.

We will begin by looking at Nikolai Kulemin as an example. In Part II, we predicted that Kulemin should see similar ice time to last year, approximately 17:19/GP. We will begin by converting this number to seconds, to eliminate the colon. Thus Kulemin’s 17:19 is now expressed as 1039 seconds of Ice time per game.

In Part II we were able to predict the amount of shots he would take based on his career averages. Now we will be estimating how many shots he will personally allow while on the ice. One might say that this is subjective as there are 5 people on the ice at any point that a shot is taken, so they cannot specifically be counted against him—this will be accounted for.

To determine how many shots Kulemin will be responsible for, we must calculate his projected season ice time. We thus take the 74 games we predicted him to play, and multiply this by the 1039 seconds of ice time he will see per game. We then divide this number by 60 (60 seconds in a minute) to set us back to the total number of minutes we can expect (in 74 games). This number is 1281.43 for Kulemin.

Using the information in the tables above, we see that at even strength, Kulemin allows 27.1 Shots Against per 60 minutes of play. With a whole team of Kulemins, this would put us in the elite teams of the league. Unfortunately there are players like Joffery Lupul who allow 33.5 shots against per game. This shows us that not only did the loss of Beauchemin hurt us, but the addition of Lupul also hurt us in this department. Anyway, we will take total season ice time, and divide first by 60 to convert the minutes to a per 60 figure TOI/60, and then divide the total number by 60 so that it is consistent with our next calculation, giving us currently (TOI/60)/60. We will then take this whole number and multiply it by Kulemin’s SOGA/60, giving us ((TOI/60)/60)*SOGA/60

Or if we look at it another way, these are the measurements: ((Av Minutes/60)/60)*SA/60 or another way, (Av. Hours [decimal]/60)/60*SA/60. The two /60’s cancel each other out, and thus we are left with the ice time Kulemin is given over a game’s time, and the number of shots that are likely to be taken against, over the entire hour of the hockey game, while he is on the ice. For Kulemin, this number is 9.65. So we can expect about 9.65 shots against the Leafs over the entire game, while Kulemin is on the ice.

Now if we were to add up the total number of shots against for each player, we would probably end up with something around 200 shots against, which is complete false. For this reason, we have to factor in the number of players on the ice. Our next calculation will be to divide Kulemin’s 9.65 with the number of players on ice (per situation). So, at Even Strength we divide 9.65 by 5, at PP we also divide by 5, and at PK we will divide by 4. Running this equation, we have a value of 1.98 at even strength. This value indicates the number of shots we can directly attribute to Kulemin on a per game basis.

When we multiple Kulemin’s 1.98 shots against by his 74 Games, we determine that he allows 147 Shots against over the whole season.

Total TOI (m) = (TOI/GP (s)*(GP)/60 à Total Time On Ice Per Season in Minutes
SOGA/TOI/60 = [(Total TOI (m))/60/60]*(SOGA/60) à Number of Shots against per Game while the player is on the ice.
Average SA/GP = (SOGA/TOI/60)/(# Players on Ice) à This tells us how many shots are directly a result of our player’s ice time.
Total SA = (Average SA/GP)*(GP) à Number of Shots Against over a season that can be attributed to the player.

This gives us the following statistics at even strength:

 Name GP Even Strength TOI/GP (s) Total TOI (m) SOGA/60 SOGA/TOI/60 Average SA/GP Total SA Joffrey Lupul 74 1071 1320.9 33.5 12.29170833 2.458341667 182 Tim Connolly 60 1178 1178 25.9 8.475055556 1.695011111 102 Phil Kessel 74 1178 1452.8667 31.1 12.5511537 2.510230741 186 Clarke MacArthur 74 1026 1265.4 27.1 9.52565 1.90513 141 Mikhail Grabovski 74 1161 1431.9 26.5 10.540375 2.108075 156 Nikolai Kulemin 74 1039 1281.4333 27.1 9.64634537 1.929269074 143 Nazem Kadri 74 1135 1399.8333 28.3 11.00424537 2.200849074 163 Tyler Bozak 74 967 1192.6333 30.5 10.10425463 2.020850926 150 Colby Armstrong 74 967 1192.6333 27.6 9.143522222 1.828704444 135 Mike Brown 74 605 746.16667 28.8 5.969333333 1.193866667 88 Colton Orr 74 304 374.93333 26.5 2.759925926 0.551985185 41 Darryl Boyce 74 655 807.83333 30 6.731944444 1.346388889 100 Joey Crabb 64 655 698.66667 31.3 6.074518519 1.214903704 78 Matthew Lombardi* 22 1156 423.86667 39.4 4.638985185 0.927797037 20 Joe Colborne 24 946 378.4 29.5 3.100777778 0.620155556 15 Carl Gunnarsson 74 1425 1757.5 28.1 13.71826389 2.743652778 203 Dion Phaneuf 74 1518 1872.2 29.2 15.18562222 3.037124444 225 John Michael Liles 74 1348 1662.5333 25.4 11.7300963 2.346019259 174 Luke Schenn 74 1342 1655.1333 28.5 13.10313889 2.620627778 194 Keith Aulie 74 817 1007.6333 33.6 9.404577778 1.880915556 139 Cody Franson 74 890 1097.6667 29.2 8.903296296 1.780659259 132 Mike Komisarek 48 817 653.6 29.9 5.428511111 1.085702222 52 Totals 82 60.613984 29.409 2817

Now looking at the totals, the 82 was simply an average calculation to ensure that we have the appropriate number of games played, which is true. The 60.613... is the average number of minutes played per game, and the 29.4 is the average shots against per 60. Finally, the 2911 shots against is reflective of the number of shots we can expect at even strength. This total is atrocious. The reason we don’t need to worry about it though is because it only looks at even strength, and while there are more shots allowed on the penalty kill, there are much less allowed on the powerplay. For this reason, we will have to perform the same calculations at 5v4 and 4v5.

 Name GP Penalty Kill TOI/GP (s) Total TOI (m) SOGA/60 SOGA/TOI/60 Average SA/GP Total SA Joffrey Lupul 74 0 0 0 0.00000 0 0 Tim Connolly 60 103 103 47.7 1.36475 0.3411875 20 Phil Kessel 74 7 8.6333333 56.3 0.13502 0.033753935 2 Clarke MacArthur 74 0 0 0 0.00000 0 0 Mikhail Grabovski 74 60 74 32.8 0.67422 0.168555556 12 Nikolai Kulemin 74 55 67.833333 35 0.65949 0.164872685 12 Nazem Kadri 74 0 0 0 0.00000 0 0 Tyler Bozak 74 108 133.2 44.8 1.65760 0.4144 31 Colby Armstrong 74 111 136.9 40.3 1.53252 0.383129861 28 Mike Brown 74 95 117.16667 37.1 1.20747 0.301866898 22 Colton Orr 74 0 0 0 0.00000 0 0 Darryl Boyce 74 21 25.9 40 0.28778 0.071944444 5 Joey Crabb 64 48 51.2 39.8 0.56604 0.141511111 9 Matthew Lombardi* 22 0 0 0 0.00000 0 0 Joe Colborne 24 0 0 0 0.00000 0 0 0 Carl Gunnarsson 74 156 192.4 40.6 2.16984 0.542461111 40 Dion Phaneuf 74 117 144.3 38.9 1.55924 0.389810417 29 John Michael Liles 74 63 77.7 28.2 0.60865 0.1521625 11 Luke Schenn 74 164 202.26667 40.3 2.26426 0.566065741 42 Keith Aulie 74 124 152.93333 43.8 1.86069 0.465172222 34 Cody Franson 74 0 0 0 0.00000 0 0 Mike Komisarek 48 93 74.4 40.3 0.83287 0.208216667 10 Totals 82 4.761687 40.393 310

 Name GP Power Play TOI/GP (s) Total TOI (m) SOGA/60 SOGA/TOI/60 Average SA/GP Total SA Joffrey Lupul 74 216 266.4 10 0.74 0.148 11 Tim Connolly 60 169 169 9.4 0.441277778 0.088255556 5 Phil Kessel 74 225 277.5 8.7 0.670625 0.134125 10 Clarke MacArthur 74 175 215.83333 8.3 0.497615741 0.099523148 7 Mikhail Grabovski 74 188 231.86667 7.3 0.470174074 0.094034815 7 Nikolai Kulemin 74 159 196.1 7.8 0.424883333 0.084976667 6 Nazem Kadri 74 155 191.16667 10.6 0.56287963 0.112575926 8 Tyler Bozak 74 177 218.3 9.6 0.582133333 0.116426667 9 Colby Armstrong 74 36 44.4 9.9 0.1221 0.02442 2 Mike Brown 74 0 0 0 0 0 0 Colton Orr 74 0 0 0 0 0 0 Darryl Boyce 74 20 24.666667 7.8 0.053444444 0.010688889 1 Joey Crabb 64 50 53.333333 4.5 0.066666667 0.013333333 1 Matthew Lombardi* 22 145 53.166667 6 0.088611111 0.017722222 0 Joe Colborne 24 235 94 10.6 0.276777778 0.055355556 1 0 Carl Gunnarsson 74 80 98.666667 7.5 0.205555556 0.041111111 3 Dion Phaneuf 74 235 289.83333 9.8 0.788990741 0.157798148 12 John Michael Liles 74 264 325.6 6.5 0.587888889 0.117577778 9 Luke Schenn 74 59 72.766667 7.3 0.14755463 0.029510926 2 Keith Aulie 74 14 17.266667 6.4 0.030696296 0.006139259 0 Cody Franson 74 113 139.36667 6.9 0.267119444 0.053423889 4 Mike Komisarek 48 0 0 0 0 0 0 Totals 82 7.2664228 99

With all these calculations in place, we can use a final calculation to determine the approximate shot totals we can expect against next year:

Shots Against = ((ES Shots/Predicted TOI)*(Predicted TOI-PP TOI-PK TOI))+PP SA+PK SA

This equation takes our total shots at even strength and establishes a Shots/Minute ratio. We must multiply this ration by the total predicted ice time less the powerplay ice time and the penalty kill ice time. After this, we add the powerplay shots against and the penalty kill shots against, to determine our total shots against. When we include the numbers:

Shots Against =((2817/60.614)*(60.614-7.2664-4.76169))+99+310
Shots against = 2667

There is just one problem with this statistic:

Because of the new additions of players like Franson, Liles, Connolly,etc. Who have powerplay ice time based on their past teams, and because of the prediction-nature of these time on ice totals, our total time on ice values are a little bit off.

The above data stats that the leafs will play an average of 60.614 minutes per game, including 4.76169 Penalty Kill time and 7.2664 Power play time.

I am unable to find exact data on the average minutes played by the leafs last season, but this is what we can conclude: last season the leafs went to overtime 18 times, and to the shootout 11 of these times. Thus, our total number of minutes is (64*60)+(11*65)+the ice time of the last 7 games. Below are those games:

 Date Opponent Result TOI October 15, 2010 New York Rangers 4-3 Win 63:08 October 18, 2010 New York Islanders 2-1 Loss 63:26 November 30, 2010 Tampa Bay Lightning 4-3 Loss 61:15 February 10,2011 New Jersey Devils 2-1 Loss 64:36 February 27, 2011 Atlanta Thrashers 3-2  Loss 62:31 March 2, 2011 Pittsburgh Penguins 3-2 Win 60:42 March 8, 2011 New York Islanders 4-3 Win 64:02

Using these numbers, our average ice time becomes:
= ((64*60)+(11*65)+63.13+63.43+61.25+64.6+62.52+60.7+64.03)/82
= 4994.66/82
= 60.91

The Powerplay and Penalty Kill data is available on NHL.com, however, and was on average 6.67/GP and 5.48/GP, respectively. We can now use our accurate totals to determine the true values expected. When we factor in the true total ice time, the increase in penalty kill time, and the decrease in powerplay time, we should actually expect 2714 Shots Against.

And just to verify these stats, we have one more metric we can use to estimate our special teams time. The far left columns express the average penalties drawn and taken by each player per 60 minutes.

 Name GP TOI/GP (s) PTAKE/60 PDRAW/60 (PDRAW- PTAKE)/60 Total Penalties Taken Total Penalties Drawn Pave total Joffrey Lupul 74 1071 0.4 1.4 1 6.443414634 22.552 16 Tim Connolly 60 1178 0.5 0.5 0 7.182926829 7.18293 0 Phil Kessel 74 1178 0.3 0.9 0.6 5.315365854 15.9461 11 Clarke MacArthur 74 1026 0.5 1.2 0.7 7.715853659 18.518 11 Mikhail Grabovski 74 1161 1.1 1 -0.1 19.20841463 17.4622 -2 Nikolai Kulemin 74 1039 0.5 0.9 0.4 7.813617886 14.0645 6 Nazem Kadri 74 1135 0.5 2.2 1.7 8.535569106 37.5565 29 Tyler Bozak 74 967 0.3 0.9 0.6 4.363292683 13.0899 9 Colby Armstrong 74 967 0.6 1.2 0.6 8.726585366 17.4532 9 Mike Brown 74 605 0.6 0.6 0 5.459756098 5.45976 0 Colton Orr 74 304 3.4 0.5 -2.9 15.54601626 2.28618 -13 Darryl Boyce 74 655 1.5 1 -0.5 14.77743902 9.85163 -5 Joey Crabb 64 655 1.1 0.4 -0.7 9.372357724 3.40813 -6 Matthew Lombardi* 22 1156 0 0 0 0 0 0 Joe Colborne 24 946 0 0 0 0 0 0 0 0 0 Carl Gunnarsson 74 1425 0.4 0.6 0.2 8.573170732 12.8598 4 Dion Phaneuf 74 1518 0.7 0.5 -0.2 15.98219512 11.4159 -5 John Michael Liles 74 1348 0.3 0.8 0.5 6.082439024 16.2198 10 Luke Schenn 74 1342 0.4 0.3 -0.1 8.073821138 6.05537 -2 Keith Aulie 74 817 0.9 0.3 -0.6 11.05939024 3.68646 -7 Cody Franson 74 890 0.8 0.1 -0.7 10.70894309 1.33862 -9 Mike Komisarek 48 817 1.1 0.3 -0.8 8.767804878 2.39122 -6 Totals 82 189.71 238.80 49.09 4.63 5.82 1.20

In our ‘Total Penalties Taken’ column, we divide the TOI/GP in seconds by 60, to give us the number of minutes each player plays each game. We then multiply this number of the percentage of the season they play (GP/82), and multiply it again by their PTAKE/60. The same process is done for PDRAW.

I will have to come back to this section, as I believe there is something extremely wrong with this data. If you look, Colton Orr only has a 15.54 penalties. Perhaps these 15.54 fighting majors, but that still seems low. As I said, I will have to return here. Regardless, the important part is the totals at the bottom. If we look at the first set of totals, it is the sum of all the total columns. They seem low, but the data is normalized so if the penalties taken are low, the penalties drawn are also low. The second set of totals takes the first, divides by 82 games, and multiplies by an average of 2 minutes per penalty, giving us 4.63PK minutes and 5.82PP minutes per game. These numbers are eerily similar to the numbers we actually had this season.

The real strength behind this analysis can be seen through the ratio. If we look at last season’s 6.67PP and 5.48PK, and divide one by the other, we have a ratio of 1.217 PP minutes for each PK minute. If we look at our projected totals, it comes out to 1.2 PP minutes. In addition, if we divide our projected PP by PK, we come out with 1.257 PP minutes—again, pretty similar. What this tells me is not that we should change the totals, but that we can be confident with our projection.

Regardless, I have run the same final calculation, factoring in the average number of shots allowed by each player per game, in every situation, and the time they play in each situation. If we use the new totals of 4.65PK minutes and 5.82PP minutes, our total shots against over the whole season is 2703—slightly less than last season.

Projections of any number between 2667-2713 can be made with confidence, and a slight deviation from these numbers should also be expected just because of chance. I will run five different calculations to determine an expected ranking, and we will be able to make a judgment on our final Win Threshold.

 Shots Against Equation Win Threshold League Rank Conference Rank 2744 (2644-240)/2644 0.9092 15th 8th 2667 (2667-240)/2667 0.9100 16th 8th 2690 (2690-240)/2690 0.9108 17th 8th 2713 (2713-240)/2713 0.9115 17th 8th 2746 (2736-240)/2736 0.9123 17th 8th

Just to explain this, 2690 is an average of 32.80 shots per game over the whole season. This would rank the Leafs 29th in the league, 0.3 shots per game better than the 30th team, and 0.2 worse than 28th. What I’m saying is that while the stats paint this picture, it is unlikely that this will actually occur. Perhaps the new assistant coaches, Aulie’s improvement, an improvement on Lombardi’s stats, etc. will help improve this number.

This is the problem with posts like these: they are completely hypothetical. Regardless, I am enjoying putting these together, and I hope it was informative. As you can see, with the goal output we anticipated in Part II, and the shots against we expect here, we will rank 8th in the conference for Win Threshold, and 17th in the league.

Part IV, the final part, will look at our goaltending situation and whether or not we can reach our Win Threshold.

PensionPlanPuppets.com is a fan community that allows members to post their own thoughts and opinions on the Toronto Maple Leafs and hockey in general. These views and thoughts may not be shared by the editor of PensionPlanPuppets.com.

## Trending Discussions

forgot?

As part of the new SB Nation launch, prior users will need to choose a permanent username, along with a new password.

I already have a Vox Media account!

### Verify Vox Media account

As part of the new SB Nation launch, prior MT authors will need to choose a new username and password.

We'll email you a reset link.

Try another email?

### Almost done,

By becoming a registered user, you are also agreeing to our Terms and confirming that you have read our Privacy Policy.

### Join Pension Plan Puppets

You must be a member of Pension Plan Puppets to participate.

We have our own Community Guidelines at Pension Plan Puppets. You should read them.

### Join Pension Plan Puppets

You must be a member of Pension Plan Puppets to participate.

We have our own Community Guidelines at Pension Plan Puppets. You should read them.