AllegSkill

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AllegSkill is a system for rating the skill of Allegiance players based on their overall performance in-game. AllegSkill is based on the Trueskill system developed by Microsoft Research (who also developed Allegiance) with some notable additions. The term 'AllegSkill' is intended to refer to the entire system, which includes additional statistics, and Microsoft Research should not be held responsible for differences when and where the occur.

Technical details

What follows is the simplest incarnation of the Trueskill update algorithm, as used for commander ratings. We've provided as much information as is sensible, and we only assume that the reader is familiar with (or able to look up) the error function (<math>erf</math>).

This scenario pits a newbie commander (<math>\,\!\mu_l = 25; \sigma_l = 8.333...</math>) against a slightly more experienced commander (<math>\,\!\mu_w = 32; \sigma_w = 5</math>) in a match where the more experienced commander won.

Now let's put some letters and functions on the field:

<math>\beta = \frac{25}{6} </math> is the standard variance around performance

<math>\gamma = \frac{25}{300}</math> is the dynamics variable, which prevents sigma from ever reaching zero

<math>\varepsilon \simeq 0.08</math> is derived empirically from the percentage of games which result in a draw, currently ~1.01%.

<math>\text{PDF}(x):=\frac{1}{\sqrt{2\pi}} \cdot e^{-\frac{x^2}{2}}</math>

PDF(x) is the probability density function of a normal distribution with a mean of zero and variance of one.

We will also use its cumulative distribution function:

<math>\text{CDF}(y)=\frac{1}{2}+\frac{1}{2}\text{erf}\left( \frac{1}{\sqrt{2}} y\right)</math>.

The Trueskill update functions follow:

<math>V_{win}(t,\varepsilon ):=\frac{PDF(t-\varepsilon )}{CDF(t-\varepsilon )}</math>

<math>W_{win}(t,\varepsilon ):=V_{win}(t,\varepsilon )\cdot \left( V_{win}(t,\varepsilon )+t-\varepsilon \right)</math>

<math>\mu _{w}=32,\sigma _{w}=5,\mu _{l}=25,\sigma _{l}=\frac{25}{3}</math>

<math>c=\sqrt{2\beta ^{2}+\sigma _{w}^{2}+\sigma _{l}^{2}} = \frac{5}{6}\sqrt{186}</math>

<math>\mu '_{w}=\mu _{w}+\frac{\sigma _{w}^{2}}{c}\cdot V_{win}\left( \frac{\mu _{w}-\mu _{l}}{c},\frac{\varepsilon }{c} \right)</math>

<math>\mu '_{w}=32+\frac{\text{0}\text{.09195636321}\sqrt{186}\sqrt{2}}{\sqrt{\pi }}</math>

<math>\,\!\mu '_{w}=\text{33}\text{.00064106}</math>

<math>\sigma '_{w}=\sqrt{\sigma _{w}^{2}\left( 1-\frac{\sigma _{w}^{2}}{c^{2}}\cdot W_{win}\left( \frac{\mu _{w}-\mu _{l}}{c},\frac{\varepsilon }{c} \right) \right)+\gamma ^{2}}</math>

<math>\sigma '_{w}=\sqrt{\frac{3601}{144}-\frac{\text{2}\text{.758690898}\sqrt{2}\left( \frac{\text{0}\text{.5701294519}\sqrt{2}}{\sqrt{\pi }}+\text{0}\text{.04463650105}\sqrt{186} \right)}{\sqrt{\pi }}}</math>

<math>\,\!\sigma '_{w}=\text{4}\text{.760851650}</math>

<math>\mu '_{l}=\mu _{l}-\frac{\sigma _{l}^{2}}{c}\cdot V_{win}\left( \frac{\mu _{w}-\mu _{l}}{c},\frac{\varepsilon }{c} \right)</math>

<math>\mu '_{l}=25-\frac{\text{0}\text{.2554343423}\sqrt{186}\sqrt{2}}{\sqrt{\pi }}</math>

<math>\,\!\mu '_{l}=\text{22}\text{.22044149}</math>

<math>\sigma '_{l}=\sqrt{\sigma _{l}^{2}\left( 1-\frac{\sigma _{l}^{2}}{c^{2}}\cdot W_{win}\left( \frac{\mu _{w}-\mu _{l}}{c},\frac{\varepsilon }{c} \right) \right)+\gamma ^{2}}</math>

<math>\sigma '_{l}=\sqrt{\frac{10001}{144}-\frac{\text{21}\text{.28619519}\sqrt{2}\left( \frac{\text{0}\text{.5701294519}\sqrt{2}}{\sqrt{\pi }}+\text{0}\text{.04463650105}\sqrt{186} \right)}{\sqrt{\pi }}}</math>

<math>\,\!\sigma '_{l}=\text{7}\text{.168423552}</math>

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