方法说明

逐层构建评分变动

Pick two clubs, choose a result, then walk through the rating update from the simplest Elo version to the full beyondELO match adjustment.

方法内容跟随所选评分系统

After every layerParis Saint-Germain +11.6

66.1% Paris Saint-Germain / 33.9% Bayern Munich

主队Paris Saint-Germain#1

ELO 评分

客队Bayern Munich#2

ELO 评分

Paris Saint-Germain

2-1

Bayern Munich

Step 1

Start with only the two ratings

The two ELO ratings set the win probability before the result moves points.

R_{n e w} = R_{o l d} + K (W - W_{e})

了解此步骤

此时的计算Paris Saint-Germain +9.5

Paris Saint-Germain52.3%

Bayern Munich47.7%

Paris Saint-Germain2045 -> 2055+9.5

Bayern Munich2029 -> 2019-9.5

这是加入比赛背景前的基础值。

Only the two starting ratings and the chosen result are active here. Every later step changes this same exchange.

Step 2

Add match importance with adaptive K

Competition and round weights replace a single base K.

K_{ba se} = f (co m p e t i t i o n, s t a g e)

了解此步骤

此时的计算Paris Saint-Germain +14.3

Paris Saint-Germain52.3%

Bayern Munich47.7%

Paris Saint-Germain2045 -> 2059+14.3

Bayern Munich2029 -> 2015-14.3

This layer changes Paris Saint-Germain by +4.8 Elo compared with the previous step.

资格赛

正赛

赛事Preliminary1st Round2nd Round3rd RoundQualifying playoff

UCL

UEL

UECL

历史第四轮比赛使用资格赛附加赛数值。

赛事Group phaseEarly knockoutLate knockout

UCL

UEL

UECL

Step 3

Give the home team its venue edge

Home advantage is added before probability, and removed at neutral venues.

W_{e} = \frac{1}{1 + 1 0 ^{(R_{a w a y} - (R_{h o m e} + H F A)) /400}}

了解此步骤

此时的计算Paris Saint-Germain +10.2

Paris Saint-Germain66.1%

Bayern Munich33.9%

Paris Saint-Germain2045 -> 2055+10.2

Bayern Munich2029 -> 2019-10.2

This layer changes Paris Saint-Germain by -4.1 Elo compared with the previous step.

主场优势中立场地

Step 4

Let the scoreline describe the result

Goal margin scales the move while extra time and penalties soften it.

G = 3 max (1, g o a l ma r g in)

了解此步骤

此时的计算Paris Saint-Germain +10.2

Paris Saint-Germain66.1%

Bayern Munich33.9%

Paris Saint-Germain2045 -> 2055+10.2

Bayern Munich2029 -> 2019-10.2

This layer changes Paris Saint-Germain by 0.0 Elo compared with the previous step.

Paris Saint-GermainBayern Munich

Step 5

Move uncertain teams faster

Own uncertainty speeds movement while opponent uncertainty weakens the evidence.

K_{t e am} = K_{ba se} \times G \times R D_{o w n} \times R e l iabi l i t y (R D_{o pp})

了解此步骤

此时的计算Paris Saint-Germain +11.6

Paris Saint-Germain66.1%

Bayern Munich33.9%

Paris Saint-Germain2045 -> 2057+11.6

Bayern Munich2029 -> 2017-12.0

This layer changes Paris Saint-Germain by +1.4 Elo compared with the previous step.

Paris Saint-Germain uncertaintyBayern Munich uncertainty

Paris Saint-Germain move x1.15 / x0.99 own uncertainty / opponent reliability

Bayern Munich move x1.20 / x0.99 own uncertainty / opponent reliability

Step 6

Put the full model together

The final exchange combines importance, venue, scoreline, and rating certainty.

R_{n e w} = R_{o l d} + K_{t e am} (W - W_{e})

K_{t e am} = K_{ba se} \times G \times R D_{o w n} \times R e l iabi l i t y (R D_{o pp})

W_{e} = \frac{1}{1 + 1 0 ^{(R_{a w a y} - (R_{h o m e} + H F A)) /400}}

R D_{o w n} = min (3, (\frac{max ( R D , 60 )}{60})^{0.6})

R e l iabi l i t y (R D_{o pp}) = \frac{g ( R D _{o pp} )}{g ( 60 )}, g (R D) = \frac{1}{1 + 3 ( R D /173.7178 ) ^{2} / π ^{2}}

了解此步骤

正在加载最新 UCL 示例。

逐层构建评分变动

从任意两队开始

Start with only the two ratings

Add match importance with adaptive K

Give the home team its venue edge

Let the scoreline describe the result

Move uncertain teams faster

Put the full model together