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 layerArsenal +12.7

62.7% Arsenal / 37.3% Bayern Munich

主队Arsenal#1

ELO 评分

客队Bayern Munich#2

ELO 评分

Arsenal

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Arsenal +9.9

Arsenal50.4%

Bayern Munich49.6%

Arsenal2066 -> 2076+9.9

Bayern Munich2063 -> 2053-9.9

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Arsenal +14.9

Arsenal50.4%

Bayern Munich49.6%

Arsenal2066 -> 2081+14.9

Bayern Munich2063 -> 2048-14.9

This layer changes Arsenal by +5.0 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Arsenal +11.2

Arsenal62.7%

Bayern Munich37.3%

Arsenal2066 -> 2077+11.2

Bayern Munich2063 -> 2052-11.2

This layer changes Arsenal by -3.7 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Arsenal +11.2

Arsenal62.7%

Bayern Munich37.3%

Arsenal2066 -> 2077+11.2

Bayern Munich2063 -> 2052-11.2

This layer changes Arsenal by 0.0 Elo compared with the previous step.

ArsenalBayern 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Arsenal +12.7

Arsenal62.7%

Bayern Munich37.3%

Arsenal2066 -> 2079+12.7

Bayern Munich2063 -> 2050-13.2

This layer changes Arsenal by +1.5 Elo compared with the previous step.

Arsenal uncertaintyBayern Munich uncertainty

Arsenal move x1.15 / x0.99 own uncertainty / opponent reliability

Bayern Munich move x1.19 / 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