Metodoloji anlatımı

Reyting değişimini katman katman oluşturun

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

Metodoloji seçilen reyting sistemini izler

After every layerParis Saint-Germain +11.6

66.1% Paris Saint-Germain / 33.9% Bayern Munich

Ev sahibiParis Saint-Germain#1

ELO reytingi

DeplasmanBayern Munich#2

ELO reytingi

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})

Bu adım hakkında daha fazla bilgi

Bu noktadaki hesaplamaParis Saint-Germain +9.5

Paris Saint-Germain52.3%

Bayern Munich47.7%

Paris Saint-Germain2045 -> 2055+9.5

Bayern Munich2029 -> 2019-9.5

Bu, maç bağlamı eklenmeden önceki temiz temeldir.

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)

Bu adım hakkında daha fazla bilgi

Bu noktadaki hesaplamaParis 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.

Elemeler

Ana turnuva

TurnuvalarPreliminary1st Round2nd Round3rd RoundQualifying playoff

UCL

UEL

UECL

Tarihî Dördüncü Tur maçları eleme play-off değerini kullanır.

TurnuvalarGroup 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}}

Bu adım hakkında daha fazla bilgi

Bu noktadaki hesaplamaParis 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.

Ev sahibi avantajıTarafsız saha

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)

Bu adım hakkında daha fazla bilgi

Bu noktadaki hesaplamaParis 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})

Bu adım hakkında daha fazla bilgi

Bu noktadaki hesaplamaParis 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}}

Bu adım hakkında daha fazla bilgi

Tam maç geçmişinden son UCL örneği yükleniyor.

Reyting değişimini katman katman oluşturun

İki takımla başlayın

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