We now know all 48 teams that will compete in the third Champions Hockey League season. At this time, our resident statistical expert Efraim Larsson crunches the numbers of season two, and explains why the fortunes of some leagues changed radically from the inaugural 2014–15 campaign.
by Efraim Larsson
Each of the first two Champions Hockey League seasons have ended with a Swedish team raising the European Trophy. First it was Lulea Hockey, who beat Swedish Hockey League rivals Frolunda Gothenburg in the 2014–15 Final. A year later, Frolunda took revenge by beating Karpat Oulu of the Finnish Liiga in the 2015–16 Final to bring the trophy to Gothenburg.
It is clear that the Swedish clubs have been the most successful in Champions Hockey League when comparing the participating leagues, as the SHL has been at the top in pretty much every aspect during both of the two seasons played. Let me show you why!
League vs League
Comparing the leagues as I’m about to do below (and as I’ve done a several times before, which you’d be familiar with now if you’ve read my previous articles) has of course a couple positive parts, but obviously also have a few downsides.
The sample size (the amount of games played) is the fact that makes – or can make – comparisons like this a bit fragile. The more games, the more accurately the figures actually represent the quality of a team, player, or league. With fewer games, things such as lucky bounces, short trends and uncontrolled events have a bigger impact on the numbers.
Therefore, when comparing a league with 65 games played with a league that just has 4 games played leaves the whole comparison very vulnerable to misleading numbers, as the sample sizes are so different.
However, I find the point percentage (P% – taken points / possible points, in other words a percentage of the points taken of total available points) as the most reliable element when comparing leagues in a situation like this (when the leagues have such a huge different amount of games played).
Below I’ve attached the ranking when sorted by P%, and you find all the explanations for the abbreviations below the standings.
| # | LEAGUE | CT | TM | GP | GF | GA | GD | GFA | GAA | SO | GSO | W | OW | T | OL | L | P | P/TEAM | W% | P% |
| 1 | SHL | SWE | 8 | 67 | 209 | 136 | +73 | 3.12 | 2.03 | 9 | 2 | 36 | 6 | 6 | 0 | 19 | 126 | 15.75 | 62.69% | 62.69% |
| 2 | Liiga | FIN | 8 | 69 | 167 | 135 | +32 | 2.42 | 1.96 | 10 | 7 | 34 | 3 | 9 | 5 | 18 | 122 | 15.25 | 53.62% | 58.94% |
| 3 | NOR Ligaen | NOR | 2 | 14 | 36 | 27 | +9 | 2.57 | 1.93 | 2 | 1 | 7 | 0 | 1 | 1 | 5 | 23 | 11.50 | 50.00% | 54.76% |
| 4 | CZE Extraliga | CZE | 6 | 40 | 108 | 99 | +9 | 2.70 | 2.48 | 6 | 3 | 17 | 2 | 1 | 4 | 16 | 60 | 10.00 | 47.50% | 50.00% |
| 5 | NLA | SUI | 6 | 34 | 86 | 104 | -18 | 2.53 | 3.06 | 1 | 5 | 12 | 2 | 2 | 3 | 15 | 45 | 7.50 | 41.18% | 44.12% |
| 6 | DEL | GER | 6 | 36 | 94 | 99 | -5 | 2.61 | 2.75 | 2 | 5 | 13 | 3 | 1 | 0 | 19 | 46 | 7.67 | 44.44% | 42.59% |
| 7 | SVK Extraliga | SVK | 2 | 10 | 15 | 32 | -17 | 1.50 | 3.20 | 0 | 4 | 2 | 1 | 0 | 1 | 6 | 9 | 4.50 | 30.00% | 30.00% |
| 8 | EBEL | AUT | 4 | 20 | 42 | 71 | -29 | 2.10 | 3.55 | 0 | 2 | 4 | 0 | 0 | 3 | 13 | 15 | 3.75 | 20.00% | 25.00% |
| 9 | Ligue Magnus | FRA | 2 | 8 | 18 | 33 | -15 | 2.25 | 4.13 | 0 | 0 | 2 | 0 | 0 | 0 | 6 | 6 | 3.00 | 25.00% | 25.00% |
| 10 | BLR Extraliga | BLR | 1 | 4 | 4 | 7 | -3 | 1.00 | 1.75 | 1 | 1 | 1 | 0 | 0 | 0 | 3 | 3 | 3.00 | 25.00% | 25.00% |
| 11 | DEN Ligaen | DEN | 1 | 4 | 8 | 17 | -9 | 2.00 | 4.25 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 3 | 3.00 | 25.00% | 25.00% |
| 12 | EIHL | GBR | 2 | 8 | 15 | 42 | -27 | 1.88 | 5.25 | 0 | 1 | 1 | 0 | 0 | 0 | 7 | 3 | 1.50 | 12.50% | 12.50% |
CT = Country, TM = Teams from league, GP = Games Played, GF = Goals For, GA = Goals Against, GD = Goal Difference, GFA = Goals-For Average, GAA = Goals-Against Average, SO = Shutouts, GSO = Games Shut Out, W = Wins, OW = Overtime/Shootout Wins, T = Ties (Playoffs only), OL = Overtime/Shootout Losses, L = Losses, P = Points, P/TEAM = Points-per-Team Average, W% = Winning Percentage, P% = Points Percentage.
Note: Overtime results in the playoffs are NOT included, such as wins/losses or goals.
Teams from the SHL and Liiga led the way this season as well in taking most points. Both, however, decreased their percentage slightly compared to last season (SHL -1.67% and Liiga -1.25%).
Except the French Ligue Magnus (last season the league was represented by the Briancon Diables Rouges, this season by the Gap Rapaces and Grenoble) that went from 0.00 to 25.00 P% this season, the league that increased their P% mostly from season 2014–15 to 2015–16 was the Norwegian Ligaen.
Last season, the Stavanger Oilers and Valerenga Oslo collected 12 points in 12 games (33.33 P%). This season, Storhamar Hamar joined Stavanger and raised the bar significantly as they played an impressive tournament. Together, the two teams collected 23 points in 14 games and became the third-best league in P% with 54.75 – an improvement of 21.43%.
The German teams from DEL also gained in P% this season, as they went from 25.00 in a quite terrible 2014–15 to 42.59 this season.
The Austrian-based EBEL had a tremendous CHL in 2014–15 as they had the third-best P% (57.14), slightly below the Finns in second spot. This season they definitely had more problems as the four teams collected only 15 points in 20 games (25.00 P%, which is a drop of 32.14 percent).
In my article after Game Days 1 and 2 I presented the same table where Danish club SonderjyskE Vojens and Belarusian entry Neman Grodno had both won the only game they played (a P% of 100.00). I noted at the time that it was unlikely either one would maintain a percentage even close to that as the tournament went on, and in fact neither team collected another point after its first game.
That’s a great example of how much sample size means!
Shooting and Save Percentages
This section will break down the efficiency of shooting and goaltending. When merging the Shooting Percentage (Sh%) and Save Percentage (Sv%) of a team – or in this case, a league – we get what we in the hockey statistics world call a PDO.
PDO is well known as always finding its way back to 100%, or at least close to 100%, over the long-term. But with a small sample size and a few games, the chance of keeping a high shooting efficiency or save percentage is, of course, higher than during a whole season.
Let’s break down the numbers.
| # | LEAGUE | CT | GP | GF | SOG | SOGAv | Sh% | GA | SOGA | SOGAAv | Sv% | PDO |
| 1 | NOR Ligaen | NOR | 14 | 36 | 385 | 27.50 | 9.35% | 27 | 461 | 32.93 | 94.14% | 103.49% |
| 2 | Liiga | FIN | 68 | 166 | 1896 | 27.88 | 8.76% | 133 | 1894 | 27.85 | 92.98% | 101.73% |
| 3 | SHL | SWE | 67 | 209 | 2180 | 32.54 | 9.59% | 136 | 1532 | 22.87 | 91.12% | 100.71% |
| 4 | BLR Extraliga | BLR | 4 | 4 | 70 | 17.50 | 5.71% | 7 | 139 | 34.75 | 94.96% | 100.68% |
| 5 | CZE Extraliga | CZE | 40 | 108 | 1177 | 29.43 | 9.18% | 99 | 1118 | 27.95 | 91.14% | 100.32% |
| 6 | DEN Ligaen | DEN | 4 | 8 | 80 | 20.00 | 10.00% | 17 | 158 | 39.50 | 89.24% | 99.24% |
| 7 | NLA | SUI | 34 | 86 | 978 | 28.76 | 8.79% | 104 | 1076 | 31.65 | 90.33% | 99.13% |
| 8 | SVK Extraliga | SVK | 10 | 15 | 198 | 19.80 | 7.58% | 32 | 370 | 37.00 | 91.35% | 98.93% |
| 9 | Ligue Magnus | FRA | 8 | 18 | 196 | 24.50 | 9.18% | 33 | 305 | 38.13 | 89.18% | 98.36% |
| 10 | DEL | GER | 36 | 94 | 1109 | 30.81 | 8.48% | 99 | 974 | 27.06 | 89.84% | 98.31% |
| 11 | EBEL | AUT | 20 | 42 | 556 | 27.80 | 7.55% | 71 | 637 | 31.85 | 88.85% | 96.41% |
| 12 | EIHL | GBR | 8 | 15 | 159 | 19.88 | 9.43% | 42 | 316 | 39.50 | 86.71% | 96.14% |
| AVERAGE: | 26.08 | 66.75 | 748.67 | 25.53 | 8.92% | 66.67 | 748.33 | 32.59 | 91.09% |
CT = Country, GP = Games Played, GF = Goals For, SOG = Shots On Goal, SOGAv = Shots On Goal Average, Sh% = Shooting Percentage, GA = Goals Against, SOGA = Shots On Goal Against, SOGAAv = SOGA Average, Sv% = Save Percentage, PDO = SG% + SAVE%
The 103.49% PDO of the Norwegian Ligaen indicates that Storhamar and Stavanger probably would’ve had a hard time keeping up their level of play over an extended period of time. In particular, many would say that Storhamar played a bit over their heads.
Conventional wisdom states that a PDO over 102% often shows that a team or league probably isn’t as good as it seems, while a PDO below 98% likely means that a team or league is better. This might explain the large swing in fortunes of EBEL clubs, which had a collective PDO of 101.90% last season but just 96.41 this season.
Of course, these stats need to be taken with a grain of salt as well.
Scoring first
157 games were played in the CHL in 2015–16 and in 98 of them, the team that scored the first goal won in regulation time. Of the remaining 59 games, 27 ended tied after three periods and 32 with a regulation win for the team that allowed the first goal.
Looking deeper into these numbers, scoring first resulted in a winning percentage of 68.79 and a points percentage of 70.28. When we compare the leagues in which teams has a combined total of at least 20 games we get the following standings.
| When Scoring First | When Opps. Score First | |||||||||||||||||||||
| # | LEAGUE | CT | GP | SF | OSF | SF% | W | OW | T | OL | L | P | P% | W% | W | OW | T | OL | L | P | P% | W% |
| 1 | Extraliga | CZE | 40 | 25 | 15 | 62.50% | 16 | 2 | 1 | 3 | 3 | 56 | 74.67% | 72.00% | 1 | 0 | 0 | 1 | 13 | 4 | 8.89% | 6.67% |
| 2 | SHL | SWE | 67 | 40 | 27 | 59.70% | 28 | 4 | 3 | 0 | 5 | 95 | 79.17% | 80.00% | 8 | 2 | 3 | 0 | 14 | 31 | 38.27% | 37.04% |
| 3 | Liiga | FIN | 69 | 39 | 30 | 56.52% | 27 | 1 | 4 | 1 | 6 | 88 | 75.21% | 71.79% | 7 | 2 | 5 | 4 | 12 | 34 | 37.78% | 30.00% |
| 4 | DEL | GER | 36 | 16 | 20 | 44.44% | 8 | 1 | 1 | 0 | 6 | 27 | 56.25% | 56.25% | 5 | 2 | 0 | 0 | 13 | 19 | 31.67% | 35.00% |
| 5 | NLA | SUI | 34 | 13 | 21 | 38.24% | 7 | 1 | 1 | 2 | 2 | 26 | 66.67% | 61.54% | 5 | 1 | 1 | 1 | 13 | 19 | 30.16% | 28.57% |
| 6 | EBEL | AUT | 20 | 5 | 15 | 25.00% | 1 | 0 | 0 | 1 | 3 | 4 | 26.67% | 20.00% | 3 | 0 | 0 | 2 | 10 | 11 | 24.44% | 20.00% |
| AVERAGE: | 266 | 138 | 128 | 51.88% | ||||||||||||||||||
CT = Country, GP = Games Played, SF = Scoring First, OSF = Opponents Score First, SF% = Score First-%, W = Wins, OW = Overtime/Shootout Wins, T = Ties (Playoffs only), OL = Overtime/Shootout Losses, L = Losses, P = Points, P% = Point Percentage, W% = Winning Percentage
It’s sorted by the metric S1% (Scoring-First Percentage), which indicates how often the teams from a specific league scored the first goal in a game. After that, you can see how the point and winning percentage (and detailed stats) are affected by scoring the first goal and by allowing the first goal (when opponents score first).
The three leagues with the most played games were also the ones with both highest S1%, P% and W% when scoring first, of which no one actually stands out more than any other. Where it starts getting really interesting here is when looking at the Czech Extraliga. They only won one game out of 15 when the opponents scored the first goal, which leads to their terrible P% and W%.
Notice that even when their opponents scored first, SHL and Liiga teams continued to deliver solid numbers.
Curiosa
- On 20 August, Frolunda defeated the Sheffield Steelers 9–1 on home ice and scored 7 goals in the third period, which was the highest number of goals in a period by a single team.
- On the same day, the Vaxjo Lakers took a 10–2 win against the Braehead Clan. That was both the hightest number of goals in a game scored by a single team (10) and by both teams combined (12).
- 10 of the 96 Group Stage games were decided by a shootout, where a total of 114 penalty shots were taken. 32 of those resulted in goals (28.07 Sh% and 71.53 Sv%). The most penalty shots in a single shootout occurred between Skelleftea AIK and HK Nitra on 27 August, where a total of 24 shots were taken, resulting in 5 goals (Skelleftea won 3–2).
- HC Kosice and Neman were the only teams that played a game where they didn’t manage a total of 10 shots on goal. Kosice recorded 9 shots on goal against Skelleftea on 6 October (lost 3–0) and Neman shot only 8 on goal against Adler Mannheim on 27 August (lost 2–1).
- The most shots on goal by a single team in a game was 58, recorded by Bili Tygri Liberec against Nitra on 3 September (won 2–0).
- The most total shots on goal by both teams in a game was 86, between TPS Turku and Storhamar on 3 November, in which TPS outshot Storhamar 51–35 and won 4–3.
- HC Davos recorded the best shooting percentage in a single game against Skelleftea on 8 December, scoring 4 goals on 13 shots for a S% of 30.77 (4–1 win). The next best was 30.43% by Tappara Tampere against EV Zug on 22 August, when they scored 7 goals on 23 shots (7–0 win).