TheStatican
Registered User
- Mar 14, 2012
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This thread extends the concepts previously explored here but with a specific and more in-depth focus on powerplay time exclusively. It is reasonable to assume that a player's powerplay goals on ice closely correlates with their actual powerplay Time On Ice (TOI). However the crucial question lies in understanding the degree of correlation, identifying the margin of error and determining whether these numbers can be reliably used to estimate a player's actual powerplay PP TOI in seasons predating the tracking of TOI (pre-1997-98). To address these questions, I have compiled a list comprising 100 full or near full-length player-seasons, ensuring a relatively large sample size for data verification. This list includes 52 of the top 100 and 89 of the top 200 seasons based on powerplay ice time per game(for forwards) since tracking began. Ice time figures were sourced from either the NHL.com website or Quanthockey. Strangely, some totals differ between the sites by a single second(rounding variations?)
The majority of player seasons in the sample were 82-game samples (67 out of 100), with only six seasons falling below 70 games. These exceptions were included due to their significance, such as having a notably high percentage of powerplay goals on ice for or featuring a substantial powerplay TOI. Analyzing seasons with missed games is challenging and very time consuming as first I needed to find out exactly which games the players missed and then I had to removed the team totals(PP goals & PP minutes) from those games from the teams full season totals, all of which was done using manually inputted dates on NHL.com. However, players with missed games could offer additional insights into how a team's powerplay performs with and without these players, a potential avenue for future exploration.
Originally, I organized the list by descending order of PP TOI. However I later reordered it based on the highest to lowest percentage of a team's powerplay goals a player was on the ice for. In my opinion this ordering makes more sense, since we don't actually know the PP TOI in seasons prior to tracking - we can ascertain the % of the teams total powerplay goals they were on ice for.
Definitions:
"PP" - where used is short for "powerplay".
"Pts" - Speaks for itself, I've listed the players point totals for that season just to help quickly identify the season in question
"Rk" - Is the all time rank of the season by PP TOI(for forwards). Any season without a Rk number is still a high powerplay TOI season but not quite within the top 200 all-time. These seasons were included due to their significance i.e. an Art Ross winning season or other very high scoring year.
"Ratio" - Is the ratio of a players estimated PP ice time which is calculated by dividing the number of PP goals the player was on the ice for verses the teams total powerplay goals and using the team totals only in the games they played in.
"Est. TOI" - Is how much ice time a player would have had if their PP ice time was proportional to the percentage of the teams PP goals they were on the ice for.
"PP+/-" - Is the number of PP goals more or less than expected that the player was on the ice for, based on their actual PP ice time.
"Accuracy" - Is the accuracy of the players ratio to the average ratio of all player-seasons above 60% team powerplay goals on ice for, which was 1 to 1.17
ConclusionsAs previously mentioned, the mean of the entire dataset yielded a ratio of 1 to 1.15 1 to 1.16 with a range spanning from 0.89 to 1.37 It should be unsurprising that star players exhibit a higher incidence of being on the ice for powerplay goals relative to their powerplay ice time. This observation aligns with the fact that during the powerplay minutes they don't participate in lower-skilled players are utilized as substitutes, with the result being lower powerplay scoring rates. Additionally, if the teams main powerplay unit fails to score during their time on the ice, they are likely to leave the ice towards the end of the powerplay once possession is lost. This in turn places the new players joining the ice for the remainder of the powerplay in a disadvantageous position for scoring i.e. mop-up duties.
The range of 0.89 to 1.37 does seem quite large but for seasons above 70% the range was narrower at 1.01 to 1.37 furthermore 79% of player-seasons(72 of 91) above the 60% mark came within 10% of the mean in that range(1.17) and all seasons above 70% are within 15% of the mean. As the number of player-seasons increase, most of whom are likely to have progressively lower percentages of team powerplay goals on ice for compared to those listed above, the ratio will approach the league norm(1 to 1). This trend is already becomes evident towards the bottom of the list when the '% of PP goals on ice for' drops below 60%
Accuracy of the results in relation to the mean:
This method does indeed offers a reasonable estimate of a player's ice time in the pre-Time On Ice (TOI) tracked era. However, it is advisable to use a ratio of 1 to 1.17 (17% less ice time than expected) rather than a 1 to 1 ratio. A potential deviation of around 10% should be considered as likely, but any deviations beyond 15% can be assumed to be extremely unlikely for high powerplay usage players.
Considering the powerplay performance of a team in games with and without a specific player may also help in narrowing down the difference in scoring rates for powerplay goals when the player is on the ice versus when they are off the ice. But further analysis is required before drawing any final conclusions in that regard.
*edit After double checking some figures I noticed a few errors in the dataset, I accidentally used the entire season team totals some players when I should have eliminated the team totals in the games they didn't play in (Kovalchuk '08 Selänne '99 & Malkin '12). Another corrected mistake was Recchi in 06-07, incorrectly had his teams total as 81 for the year when they actually had 94 which removed one of the biggest outliners. The data-set has been adjusted accordingly.
The majority of player seasons in the sample were 82-game samples (67 out of 100), with only six seasons falling below 70 games. These exceptions were included due to their significance, such as having a notably high percentage of powerplay goals on ice for or featuring a substantial powerplay TOI. Analyzing seasons with missed games is challenging and very time consuming as first I needed to find out exactly which games the players missed and then I had to removed the team totals(PP goals & PP minutes) from those games from the teams full season totals, all of which was done using manually inputted dates on NHL.com. However, players with missed games could offer additional insights into how a team's powerplay performs with and without these players, a potential avenue for future exploration.
Originally, I organized the list by descending order of PP TOI. However I later reordered it based on the highest to lowest percentage of a team's powerplay goals a player was on the ice for. In my opinion this ordering makes more sense, since we don't actually know the PP TOI in seasons prior to tracking - we can ascertain the % of the teams total powerplay goals they were on ice for.
Definitions:
"PP" - where used is short for "powerplay".
"Pts" - Speaks for itself, I've listed the players point totals for that season just to help quickly identify the season in question
"Rk" - Is the all time rank of the season by PP TOI(for forwards). Any season without a Rk number is still a high powerplay TOI season but not quite within the top 200 all-time. These seasons were included due to their significance i.e. an Art Ross winning season or other very high scoring year.
"Ratio" - Is the ratio of a players estimated PP ice time which is calculated by dividing the number of PP goals the player was on the ice for verses the teams total powerplay goals and using the team totals only in the games they played in.
"Est. TOI" - Is how much ice time a player would have had if their PP ice time was proportional to the percentage of the teams PP goals they were on the ice for.
"PP+/-" - Is the number of PP goals more or less than expected that the player was on the ice for, based on their actual PP ice time.
"Accuracy" - Is the accuracy of the players ratio to the average ratio of all player-seasons above 60% team powerplay goals on ice for, which was 1 to 1.17
Rk | Name | Season | Pts | Team PPG's | Player PPG's | Team PP TOI | Est. TOI | Actual TOI | % team PP goals | % team PP mins | Ratio | Accuracy(in relation to 1.17) |
| Draisaitl | 22-23 | 128 | 87 | 87 | 04:56 | 04:56 | 03:58 | 100.0% | 80.4% | 1.24 | 6.3% |
| McDavid | 20-21 | 105 | 48 | 47 | 04:50 | 04:43 | 04:11 | 97.9% | 86.6% | 1.13 | -3.3% |
| McDavid | 22-23 | 153 | 89 | 87 | 04:56 | 04:49 | 03:55 | 97.8% | 79.4% | 1.23 | 5.2% |
51 | Malkin | 08-09 | 113 | 62 | 60 | 07:04 | 06:50 | 05:33 | 96.8% | 78.5% | 1.23 | 5.3% |
| Draisaitl | 20-21 | 84 | 48 | 46 | 04:50 | 04:37 | 04:13 | 95.8% | 87.2% | 1.10 | -6.1% |
148 | Ovechkin | 13-14 | 79 | 66 | 63 | 05:19 | 05:04 | 05:02 | 95.5% | 94.7% | 1.01 | -13.8% |
76 | Ovechkin | 08-09 | 110 | 84 | 80 | 06:06 | 05:48 | 05:24 | 95.2% | 88.5% | 1.08 | -8.0% |
28 | Kariya | 98-99 | 101 | 83 | 79 | 07:32 | 07:10 | 05:44 | 95.2% | 76.1% | 1.25 | 6.9% |
| Draisaitl | 19-20 | 110 | 59 | 56 | 04:25 | 04:11 | 03:50 | 94.9% | 86.8% | 1.09 | -6.5% |
34 | Kovalchuk | 07-08 | 87 | 50 | 47 | 06:30 | 06:06 | 05:43 | 94.0% | 87.9% | 1.07 | -8.6% |
141 | Ovechkin | 09-10 | 109 | 63 | 59 | 05:49 | 05:26 | 05:03 | 93.7% | 86.8% | 1.08 | -7.8% |
2 | Kovalchuk | 03-04 | 87 | 59 | 55 | 08:23 | 07:48 | 06:51 | 93.2% | 81.7% | 1.14 | -2.5% |
189 | Ovechkin | 19-20 | 67 | 41 | 38 | 05:23 | 04:59 | 04:53 | 92.7% | 90.7% | 1.02 | -12.7% |
49 | Selänne | 98-99 | 107 | 76 | 70 | 07:32 | 06:56 | 05:33 | 92.1% | 73.7% | 1.25 | 6.9% |
6 | Weight | 00-01 | 90 | 59 | 54 | 08:04 | 07:22 | 06:38 | 91.5% | 82.2% | 1.11 | -4.9% |
| Malkin | 11-12 | 109 | 53 | 48 | 05:27 | 04:56 | 04:21 | 90.6% | 79.8% | 1.13 | -3.0% |
36 | Jágr | 99-00 | 96 | 42 | 38 | 07:30 | 06:47 | 05:42 | 90.5% | 76.0% | 1.19 | 1.8% |
1 | Kovalchuk | 05-06 | 98 | 91 | 82 | 10:06 | 09:06 | 08:10 | 90.1% | 80.9% | 1.11 | -4.8% |
26 | Lemieux | 02-03 | 91 | 58 | 52 | 07:10 | 06:25 | 05:47 | 89.7% | 80.7% | 1.11 | -5.0% |
77 | Savard | 06-07 | 96 | 71 | 63 | 08:10 | 07:14 | 05:24 | 88.7% | 66.1% | 1.34 | 14.7% |
194 | Bure | 00-01 | 92 | 46 | 40 | 07:18 | 06:20 | 04:52 | 87.0% | 66.7% | 1.30 | 11.5% |
32 | Jágr | 00-01 | 121 | 75 | 65 | 07:13 | 06:15 | 05:43 | 86.7% | 79.2% | 1.09 | -6.5% |
43 | Ovechkin | 07-08 | 112 | 65 | 56 | 06:44 | 05:48 | 05:36 | 86.2% | 83.2% | 1.04 | -11.5% |
70 | Kariya | 01-02 | 57 | 43 | 37 | 08:01 | 06:53 | 05:27 | 86.0% | 68.0% | 1.27 | 8.2% |
132 | Robitaille | 00-01 | 88 | 71 | 61 | 07:26 | 06:23 | 05:04 | 85.9% | 68.2% | 1.26 | 7.7% |
22 | Jágr | 98-99 | 127 | 64 | 55 | 07:34 | 06:30 | 05:50 | 85.9% | 77.1% | 1.11 | -4.7% |
4 | Ovechkin | 05-06 | 106 | 70 | 60 | 09:45 | 08:21 | 06:43 | 85.7% | 68.9% | 1.24 | 6.3% |
157 | Crosby | 09-10 | 109 | 56 | 48 | 06:29 | 05:33 | 05:00 | 85.7% | 77.1% | 1.11 | -5.0% |
19 | Fleury | 00-01 | 74 | 48 | 41 | 07:05 | 06:03 | 05:59 | 85.4% | 84.5% | 1.01 | -13.6% |
9 | Kovalchuk | 06-07 | 76 | 67 | 57 | 08:20 | 07:05 | 06:31 | 85.1% | 78.2% | 1.09 | -7.0% |
96 | Hossa | 06-07 | 100 | 67 | 57 | 08:20 | 07:05 | 05:17 | 85.1% | 63.4% | 1.34 | 14.7% |
16 | Whitney | 02-03 | 76 | 71 | 60 | 08:23 | 07:05 | 06:07 | 84.5% | 73.0% | 1.16 | -1.0% |
53 | Kovalev | 00-01 | 95 | 76 | 64 | 07:10 | 06:02 | 05:33 | 84.2% | 77.4% | 1.09 | -7.1% |
128 | Sakic | 06-07 | 100 | 79 | 66 | 07:40 | 06:24 | 05:05 | 83.5% | 66.3% | 1.26 | 7.7% |
| Bure | 97-98 | 90 | 48 | 40 | 06:59 | 05:49 | 04:30 | 83.3% | 64.4% | 1.29 | 10.5% |
151 | Malkin | 07-08 | 106 | 77 | 64 | 07:39 | 06:21 | 05:01 | 83.1% | 65.6% | 1.27 | 8.3% |
103 | Sakic | 98-99 | 96 | 65 | 54 | 08:26 | 07:00 | 05:13 | 83.1% | 61.9% | 1.34 | 14.8% |
50 | Straka | 00-01 | 95 | 76 | 63 | 07:10 | 05:56 | 05:33 | 82.9% | 77.4% | 1.07 | -8.5% |
| Kucherov | 22-23 | 113 | 71 | 58 | 05:22 | 04:23 | 04:00 | 81.7% | 74.5% | 1.10 | -6.3% |
60 | Smyth | 00-01 | 70 | 59 | 48 | 08:04 | 06:33 | 05:31 | 81.4% | 68.4% | 1.19 | 1.7% |
62 | Allison | 00-01 | 95 | 64 | 52 | 07:47 | 06:19 | 05:30 | 81.3% | 70.7% | 1.15 | -1.7% |
52 | Recchi | 99-00 | 91 | 69 | 56 | 06:55 | 05:36 | 05:33 | 81.2% | 80.2% | 1.01 | -13.6% |
| Kucherov | 18-19 | 128 | 74 | 60 | 04:57 | 04:00 | 03:43 | 81.1% | 75.1% | 1.08 | -7.7% |
177 | Yashin | 98-99 | 94 | 59 | 47 | 08:16 | 06:35 | 04:56 | 79.7% | 59.7% | 1.33 | 14.1% |
14 | Jágr | 05-06 | 123 | 83 | 66 | 08:28 | 06:43 | 06:12 | 79.5% | 73.2% | 1.09 | -7.2% |
176 | Jokinen | 05-06 | 89 | 63 | 50 | 07:58 | 06:19 | 04:56 | 79.4% | 61.9% | 1.28 | 9.5% |
23 | Crosby | 06-07 | 120 | 92 | 73 | 09:16 | 07:21 | 05:50 | 79.3% | 62.9% | 1.26 | 7.7% |
134 | Richards | 06-07 | 70 | 69 | 54 | 07:50 | 06:07 | 05:04 | 78.3% | 64.7% | 1.21 | 3.4% |
64 | Bertuzzi | 02-03 | 97 | 87 | 68 | 08:24 | 06:33 | 05:29 | 78.2% | 65.3% | 1.20 | 2.3% |
125 | Francis | 97-98 | 87 | 64 | 50 | 08:14 | 06:25 | 05:06 | 78.1% | 61.9% | 1.26 | 7.8% |
102 | Kovalev | 99-00 | 66 | 54 | 42 | 07:19 | 05:41 | 05:13 | 77.8% | 71.3% | 1.09 | -6.8% |
57 | Ovechkin | 06-07 | 92 | 67 | 52 | 08:21 | 06:28 | 05:31 | 77.6% | 66.1% | 1.17 | 0.4% |
| Kane | 15-16 | 106 | 57 | 44 | 04:54 | 03:46 | 03:07 | 77.2% | 63.6% | 1.21 | 3.7% |
92 | Fleury | 98-99 | 93 | 48 | 37 | 07:05 | 05:27 | 05:18 | 77.1% | 74.8% | 1.03 | -11.9% |
75 | Näslund | 02-03 | 104 | 87 | 67 | 08:24 | 06:28 | 05:25 | 77.0% | 64.5% | 1.19 | 2.1% |
183 | Iginla | 08-09 | 89 | 61 | 47 | 07:17 | 05:36 | 04:54 | 77.0% | 67.3% | 1.15 | -2.1% |
121 | Iginla | 01-02 | 96 | 55 | 42 | 07:43 | 05:53 | 05:08 | 76.4% | 66.5% | 1.15 | -1.9% |
| Thornton | 07-08 | 96 | 70 | 53 | 07:28 | 05:39 | 04:47 | 75.7% | 64.1% | 1.18 | 1.0% |
17 | Kariya | 05-06 | 85 | 94 | 71 | 10:28 | 07:54 | 06:06 | 75.5% | 58.3% | 1.30 | 10.8% |
180 | Kovalchuk | 02-03 | 67 | 64 | 48 | 07:39 | 05:44 | 04:55 | 75.0% | 64.3% | 1.17 | -0.3% |
| McDavid | 16-17 | 100 | 56 | 42 | 04:58 | 03:43 | 03:01 | 75.0% | 60.7% | 1.23 | 5.5% |
35 | Jágr | 06-07 | 96 | 75 | 56 | 08:09 | 06:05 | 05:43 | 74.7% | 70.1% | 1.06 | -9.0% |
203 | Fleury | 97-98 | 78 | 43 | 32 | 07:24 | 05:30 | 04:51 | 74.4% | 65.5% | 1.14 | -3.0% |
7 | Richards | 05-06 | 91 | 81 | 60 | 10:01 | 07:25 | 06:34 | 74.1% | 65.6% | 1.13 | -3.4% |
18 | Hossa | 05-06 | 92 | 100 | 74 | 10:17 | 07:36 | 06:06 | 74.0% | 59.3% | 1.25 | 6.6% |
30 | Heatley | 05-06 | 103 | 102 | 75 | 09:24 | 06:54 | 05:44 | 73.5% | 61.0% | 1.21 | 3.0% |
84 | Staal | 06-07 | 70 | 67 | 49 | 09:08 | 06:40 | 05:21 | 73.1% | 58.6% | 1.25 | 6.7% |
154 | Brind'Amour | 06-07 | 82 | 63 | 46 | 09:08 | 06:40 | 05:00 | 73.0% | 54.7% | 1.33 | 14.0% |
38 | Alfredsson | 05-06 | 103 | 99 | 72 | 09:25 | 06:50 | 05:41 | 72.7% | 60.4% | 1.21 | 3.0% |
48 | Marleau | 05-06 | 86 | 91 | 66 | 10:09 | 07:21 | 05:34 | 72.5% | 54.8% | 1.32 | 13.0% |
201 | Forsberg | 98-99 | 97 | 69 | 50 | 07:45 | 05:36 | 04:51 | 72.5% | 62.6% | 1.16 | -1.0% |
82 | Thornton | 05-06 | 125 | 94 | 68 | 09:32 | 06:53 | 05:22 | 72.3% | 56.3% | 1.29 | 9.8% |
12 | Savard | 05-06 | 97 | 100 | 71 | 10:17 | 07:18 | 06:22 | 71.0% | 61.9% | 1.15 | -2.0% |
160 | Heatley | 06-07 | 105 | 72 | 51 | 08:14 | 05:49 | 04:59 | 70.8% | 60.5% | 1.17 | 0.0% |
152 | Thornton | 06-07 | 114 | 92 | 65 | 08:15 | 05:49 | 05:01 | 70.7% | 60.8% | 1.16 | -0.7% |
91 | V.Bure | 99-00 | 75 | 59 | 41 | 06:48 | 04:43 | 05:18 | 69.5% | 77.9% | 0.89 | -23.8% |
117 | Cheechoo | 05-06 | 93 | 91 | 63 | 10:09 | 07:01 | 05:08 | 69.2% | 50.6% | 1.37 | 17.0% |
79 | Iginla | 05-06 | 67 | 87 | 60 | 09:30 | 06:33 | 05:24 | 69.0% | 56.8% | 1.21 | 3.7% |
178 | C.Lemieux | 98-99 | 51 | 71 | 49 | 07:43 | 05:19 | 04:56 | 69.0% | 63.9% | 1.08 | -7.7% |
159 | Morrison | 02-03 | 71 | 87 | 58 | 08:24 | 05:36 | 04:59 | 66.7% | 59.3% | 1.12 | -4.0% |
136 | Gomez | 05-06 | 84 | 78 | 52 | 09:11 | 06:07 | 05:04 | 66.7% | 55.2% | 1.21 | 3.3% |
108 | Recchi | 06-07 | 68 | 94 | 62 | 09:16 | 06:06 | 05:11 | 66.0% | 55.9% | 1.18 | 0.8% |
73 | Francis | 02-03 | 57 | 58 | 38 | 08:49 | 05:46 | 05:25 | 65.5% | 61.4% | 1.07 | -8.9% |
158 | Gionta | 05-06 | 89 | 78 | 51 | 09:11 | 06:00 | 05:00 | 65.4% | 54.4% | 1.20 | 2.6% |
40 | Crosby | 05-06 | 102 | 93 | 60 | 09:50 | 06:20 | 05:40 | 64.5% | 57.6% | 1.12 | -4.3% |
80 | Brind'Amour | 05-06 | 70 | 93 | 59 | 10:47 | 06:50 | 05:24 | 63.4% | 50.1% | 1.27 | 8.3% |
63 | Staal | 05-06 | 100 | 95 | 60 | 10:47 | 06:48 | 05:30 | 63.2% | 51.0% | 1.24 | 5.8% |
147 | Straka | 05-06 | 76 | 83 | 52 | 08:28 | 05:18 | 05:02 | 62.7% | 59.4% | 1.05 | -9.9% |
153 | Rolston | 05-06 | 79 | 77 | 48 | 09:05 | 05:39 | 05:01 | 62.3% | 55.2% | 1.13 | -3.5% |
170 | Satan | 01-02 | 73 | 50 | 31 | 07:44 | 04:47 | 04:58 | 62.0% | 64.2% | 0.97 | -17.5% |
156 | Stoll | 05-06 | 68 | 88 | 54 | 09:29 | 05:49 | 05:00 | 61.4% | 52.7% | 1.16 | -0.5% |
191 | Sanderson | 02-03 | 67 | 71 | 43 | 08:23 | 05:04 | 04:53 | 60.6% | 58.3% | 1.04 | -11.1% |
65 | Recchi | 05-06 | 64 | 89 | 53 | 10:15 | 06:06 | 05:28 | 59.6% | 53.3% | 1.12 | -4.6% |
44 | Näslund | 05-06 | 79 | 96 | 54 | 10:16 | 05:46 | 05:36 | 56.3% | 54.5% | 1.03 | -11.9% |
47 | Bertuzzi | 05-06 | 71 | 96 | 53 | 10:16 | 05:40 | 05:34 | 55.2% | 54.2% | 1.02 | -13.0% |
69 | Doan | 05-06 | 66 | 96 | 53 | 10:50 | 05:58 | 05:27 | 55.2% | 50.3% | 1.10 | -6.2% |
173 | O'Neill | 02-03 | 61 | 58 | 32 | 08:49 | 04:51 | 04:57 | 55.2% | 56.1% | 0.98 | -16.0% |
164 | Kariya | 06-07 | 76 | 71 | 39 | 08:32 | 04:41 | 04:58 | 54.9% | 58.2% | 0.94 | -19.3% |
85 | Sakic | 05-06 | 87 | 89 | 47 | 09:31 | 05:01 | 05:20 | 52.8% | 56.0% | 0.94 | -19.5% |
181 | Morrison | 05-06 | 56 | 96 | 44 | 10:16 | 04:42 | 04:55 | 45.8% | 47.9% | 0.96 | -18.2% |
Conclusions
| Count | Mean | Sample size in gms |
>90% | 17 | 1.140 | 1339 |
>80% | 42 | 1.161 | 3337 |
>70% | 75 | 1.177 | 5933 |
>60% | 91 | 1.169 | 7322 |
All | 100 | 1.156 | 7978 |
The range of 0.89 to 1.37 does seem quite large but for seasons above 70% the range was narrower at 1.01 to 1.37 furthermore 79% of player-seasons(72 of 91) above the 60% mark came within 10% of the mean in that range(1.17) and all seasons above 70% are within 15% of the mean. As the number of player-seasons increase, most of whom are likely to have progressively lower percentages of team powerplay goals on ice for compared to those listed above, the ratio will approach the league norm(1 to 1). This trend is already becomes evident towards the bottom of the list when the '% of PP goals on ice for' drops below 60%
Accuracy of the results in relation to the mean:
| +90% on ice for | +80% on ice for | +70% on ice for | +60% on ice for |
Within 5% | 6 of 17 (35%) | 12 of 42 (29%) | 29 of 75 (39%) | 37 of 92 (40%) |
Within 7.5% | 13 of 17 (76%) | 24 of 42 (57%) | 46 of 75 (61%) | 55 of 92 (60%) |
Within 10% | 15 of 17 (88%) | 32 of 42 (76%) | 60 of 75 (80%) | 72 of 92 (78%) |
Within 12.5% | 15 of 17 (88%) | 35 of 42 (83%) | 64 of 75 (85%) | 77 of 92 (84%) |
Within 15% | 100% | 100% | 100% | 87 of 92 (95%) |
Within 17.5% | 100% | 100% | 100% | 91 of 92 (99%) |
Range | -13.8% to +6.9% | -13.8% to +14.8% | -13.8% to +14.8% | -23.8% to +17.0% |
This method does indeed offers a reasonable estimate of a player's ice time in the pre-Time On Ice (TOI) tracked era. However, it is advisable to use a ratio of 1 to 1.17 (17% less ice time than expected) rather than a 1 to 1 ratio. A potential deviation of around 10% should be considered as likely, but any deviations beyond 15% can be assumed to be extremely unlikely for high powerplay usage players.
Considering the powerplay performance of a team in games with and without a specific player may also help in narrowing down the difference in scoring rates for powerplay goals when the player is on the ice versus when they are off the ice. But further analysis is required before drawing any final conclusions in that regard.
*edit After double checking some figures I noticed a few errors in the dataset, I accidentally used the entire season team totals some players when I should have eliminated the team totals in the games they didn't play in (Kovalchuk '08 Selänne '99 & Malkin '12). Another corrected mistake was Recchi in 06-07, incorrectly had his teams total as 81 for the year when they actually had 94 which removed one of the biggest outliners. The data-set has been adjusted accordingly.
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