Rue Salvesen

[1]:

import sys

sys.path.append("../../")

import penaltyblog as pb

Get data from football-data.co.uk

[2]:

fb = pb.scrapers.FootballData("ENG Premier League", "2019-2020")
df = fb.get_fixtures()

df.head()

[2]:

	competition	season	datetime	div	date	time	team_home	team_away	fthg	ftag	...	b365_cahh	b365_caha	pcahh	pcaha	max_cahh	max_caha	avg_cahh	avg_caha	goals_home	goals_away
id
1565308800---liverpool---norwich	ENG Premier League	2019-2020	2019-08-09 20:00:00	E0	2019-08-09	20:00	Liverpool	Norwich	4	1	...	1.91	1.99	1.94	1.98	1.99	2.07	1.90	1.99	4	1
1565395200---bournemouth---sheffield_united	ENG Premier League	2019-2020	2019-08-10 15:00:00	E0	2019-08-10	15:00	Bournemouth	Sheffield United	1	1	...	1.95	1.95	1.98	1.95	2.00	1.96	1.96	1.92	1	1
1565395200---burnley---southampton	ENG Premier League	2019-2020	2019-08-10 15:00:00	E0	2019-08-10	15:00	Burnley	Southampton	3	0	...	1.87	2.03	1.89	2.03	1.90	2.07	1.86	2.02	3	0
1565395200---crystal_palace---everton	ENG Premier League	2019-2020	2019-08-10 15:00:00	E0	2019-08-10	15:00	Crystal Palace	Everton	0	0	...	1.82	2.08	1.97	1.96	2.03	2.08	1.96	1.93	0	0
1565395200---tottenham---aston_villa	ENG Premier League	2019-2020	2019-08-10 17:30:00	E0	2019-08-10	17:30	Tottenham	Aston Villa	3	1	...	2.10	1.70	2.18	1.77	2.21	1.87	2.08	1.80	3	1

5 rows × 111 columns

Train the model

[3]:

clf = pb.models.RueSalvesenGoalModel(
    df["goals_home"], df["goals_away"], df["team_home"], df["team_away"]
)
clf.fit()

The model’s parameters

[4]:

clf

[4]:

Module: Penaltyblog

Model: Rue Salvesen

Number of parameters: 43
Log Likelihood: -1022.249
AIC: 2130.497

Team                 Attack               Defence
------------------------------------------------------------
Arsenal              1.112                -1.035
Aston Villa          0.804                -0.707
Bournemouth          0.762                -0.568
Brighton             0.744                -0.909
Burnley              0.871                -1.036
Chelsea              1.503                -0.539
Crystal Palace       0.474                -1.14
Everton              0.891                -0.742
Leicester            1.229                -1.077
Liverpool            1.586                -1.277
Man City             1.652                -1.065
Man United           1.418                -1.29
Newcastle            0.778                -0.541
Norwich              0.275                -0.733
Sheffield United     0.763                -1.387
Southampton          1.149                -0.793
Tottenham            1.166                -0.944
Watford              0.721                -0.72
West Ham             0.99                 -0.532
Wolves               1.114                -1.251
------------------------------------------------------------
Home Advantage: 0.241
Rho: -0.432
Gamma: -0.306

[5]:

clf.get_params()

[5]:

{'attack_Arsenal': 1.111931449095879,
 'attack_Aston Villa': 0.8036584045882671,
 'attack_Bournemouth': 0.7616274831177443,
 'attack_Brighton': 0.7438029939011236,
 'attack_Burnley': 0.8714749753346606,
 'attack_Chelsea': 1.5031298228608796,
 'attack_Crystal Palace': 0.473943204227092,
 'attack_Everton': 0.8905730783721502,
 'attack_Leicester': 1.2290703186815548,
 'attack_Liverpool': 1.5856160570398277,
 'attack_Man City': 1.6520840743943552,
 'attack_Man United': 1.417691292532406,
 'attack_Newcastle': 0.7783536013404229,
 'attack_Norwich': 0.2750967380076478,
 'attack_Sheffield United': 0.7634361220531481,
 'attack_Southampton': 1.148616150311251,
 'attack_Tottenham': 1.165938538146421,
 'attack_Watford': 0.7205743721370401,
 'attack_West Ham': 0.9898150953383186,
 'attack_Wolves': 1.113566228512511,
 'defence_Arsenal': -1.034889597180056,
 'defence_Aston Villa': -0.7069292337686073,
 'defence_Bournemouth': -0.5681822350334952,
 'defence_Brighton': -0.9088289955476304,
 'defence_Burnley': -1.035513131410702,
 'defence_Chelsea': -0.5386051915163717,
 'defence_Crystal Palace': -1.1399498461579118,
 'defence_Everton': -0.7423896283707103,
 'defence_Leicester': -1.0766704375173184,
 'defence_Liverpool': -1.2772161070917725,
 'defence_Man City': -1.065205892940396,
 'defence_Man United': -1.2899010102737807,
 'defence_Newcastle': -0.5410115575004688,
 'defence_Norwich': -0.7332598945150457,
 'defence_Sheffield United': -1.3868341605873409,
 'defence_Southampton': -0.7934806614414996,
 'defence_Tottenham': -0.9444089336826985,
 'defence_Watford': -0.7199536018083285,
 'defence_West Ham': -0.5319430115288346,
 'defence_Wolves': -1.2514668089034262,
 'home_advantage': 0.24101616914181714,
 'rho': -0.43193928553416344,
 'rue_salvesen': -0.3057690852799766}

Predict Match Outcomes

[6]:

probs = clf.predict("Liverpool", "Wolves")
probs

[6]:

Module: Penaltyblog

Class: FootballProbabilityGrid

Home Goal Expectation: 1.9029358346336849
Away Goal Expectation: 0.7930394339420297

Home Win: 0.5918369228555458
Draw: 0.30277883299657615
Away Win: 0.10538424213892131

1x2 Probabilities

[7]:

probs.home_draw_away

[7]:

[0.5918369228555458, 0.30277883299657615, 0.10538424213892131]

[8]:

probs.home_win

[8]:

0.5918369228555458

[9]:

probs.draw

[9]:

0.30277883299657615

[10]:

probs.away_win

[10]:

0.10538424213892131

Probablity of Total Goals >1.5

[11]:

probs.total_goals("over", 1.5)

[11]:

0.7945923403157933

Probability of Asian Handicap 1.5

[12]:

probs.asian_handicap("home", 1.5)

[12]:

0.3828536297404947

Probability of both teams scoring

[13]:

probs.both_teams_to_score

[13]:

0.5098629006377844