Let us put the tactics and game importance aside for a while and ask: what is the most important factor that decides the outcome of a game? Today we’ll talk team strength. Whether we like it or not, as much as we sometimes wish an underdog to win, the stronger teams usually do better and prevail in the long run. The ice-hockey simulation in powerplay manager is no exception.

One could write monographs about what makes a great team great. Luckily, it is easier in PPM. Each team has a profile page where you can find the estimate of team strength based on the lineup used in the previous official game. These are the ominous “stars”, the four integers indicating the levels of Goaltending, Defence, Offence and Shooting. There is a fifth one that shows the total team strength, but it is just the arithmetic mean of the former four. So let us look at the four numbers as a measure of team strength.

There is a long ongoing discussion about “what do you mean by saying that your team was much stronger”. With the scale going up to 200, it doesn’t sound like a big difference between 15 and 20, it is a basic beginners level. In the same time the difference between 15 and 20 is 25% down or 33% up, and this is no peanuts anymore. We can see whether our team is better or worse in terms of the stars, but how does it affect the chances of winning the bloody game? Be the first to know and keep reading this great feature article in the [ppm] eyrie. We bring to you the whole story as it unfolds! Blah, blah, blah!

A typical ppm ice hockey team in the middle of second season might have Goaltending GT rated at 16, Defence DF=16, Offence OF=15 and Shooting SH=14. My Eaglets have (23, 23, 22, 19); Radowan’s Enterprise is currently rated at (30,25,28,17), the best Latvian team Pardaugavas Lauvas impresses with (31,29,23,22).

We’ll do the simplest thing out there and just sum the four indicators (GT + DF + OF + SH) of both teams and compute the difference, and see how the teams perform against each other in dependence on this difference.

The results can be summarized in a table. The first column shows the difference, then the percentage of wins (in regular time), overtimes and losses of the stronger team and finally the number of games used to calculate the “odds”.

…

Diff | W % | OT % | L % | N |
---|---|---|---|---|

1-2 | 47.3 | 15.0 | 37.5 | 7588 |

3-4 | 51.8 | 14.5 | 33.5 | 7176 |

5-6 | 56.8 | 14.2 | 28.9 | 6153 |

7-8 | 62.0 | 13.4 | 24.4 | 5363 |

9-10 | 68.0 | 11.9 | 20.0 | 4609 |

11-12 | 72.3 | 11.0 | 16.6 | 3906 |

13-14 | 77.5 | 10.1 | 12.3 | 3412 |

15-16 | 81.7 | 8.0 | 10.2 | 2865 |

17-18 | 84.4 | 8.0 | 7.4 | 2298 |

19-20 | 88.6 | 6.9 | 4.4 | 1859 |

21-22 | 90.2 | 5.7 | 3.9 | 1512 |

23-24 | 92.9 | 4.2 | 2.7 | 1199 |

25-26 | 94.6 | 2.4 | 2.9 | 892 |

27-28 | 95.8 | 1.6 | 2.5 | 718 |

29-30 | 96.8 | 2.2 | 0.9 | 540 |

31+ | 99.8 | 0.1 | 0.0 | 5199 |

…

The trend is visible, isn’t it? The advantage of some 6-7 team strength points weighs approximately as much as the correct counter-tactics. I hope to get to the corresponding effect of game importance in a future article.

How does this info add to the understanding of the game? Let’s speculate and assume that I am to throw my 87 points against Liepinsh’s 105. Under normal conditions I’m at -18 meaning that my chances of winning the game are somewhere around 7.4%. Can I influence my odds? Sure, I can choose the game importance and tactics. With the right counter-tactics, the odds would shift in my favor, perhaps the shift is worth as much as 6 points making the gap approx. -12 points wide. Why, according to this arithmetics, the chance of winning just sky-rocketed to 16%! If I am lucky and my team actually plays that tactics well and if I use higher game importance, my chances might improve even further. (Of course, the life is never as simple as that) 🙂

Just want to make a final remark. Perhaps I am looking at the wrong thing. The quotient of team strength indicators might be more important than the difference. Say, is the difference between 110 and 100 points “ten points” or “ten percent” wide? Is it as good as 20 vs 10 (difference) or as good as 11 vs 10 (quotient)? Or is it something in between? I don’t know the answer, but some day…

Hi, I think the only drawback of your research is that you should have considered quotients instead of absolute differences. However, great job, it was a very valuable information. Keep up a good work.

Comment by radowan — September 17, 2009 @ 6:39 pm

Yesistrue! I just checked the data and it comes out that the odds depend on the team rating as well as on the difference, so in the next update I’ll use the quotients!

Where ppm average winning odds go as

47.3, 51.8, 56.8, 62.0, 68.0

for games where both teams have summary rating 60+ it goes less steep:

46.0, 49.6, 55.1, 59.0, 63.2

and for difference of 17-18 points it is just 73.9 instead of 84.4. This is a nice thing preventing the total dominance of a few top teams 🙂

Comment by glanvalleyeaglets — September 18, 2009 @ 12:57 pm

Thanks for the update! 🙂 Good work again… I am very curious about the results of your research on high/normal/low match importance 🙂

Comment by radowan — September 18, 2009 @ 7:31 pm

Keep up the fantastic work!

Comment by canucks357 — November 2, 2009 @ 8:31 pm