Poisson

[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 a basic poisson model

[3]:

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

The model’s parameters

[4]:

clf

[4]:

Module: Penaltyblog

Model: Poisson

Number of parameters: 41
Log Likelihood: -1057.712
AIC: 2197.424

Team                 Attack               Defence
------------------------------------------------------------
Arsenal              1.133                -0.937
Aston Villa          0.84                 -0.618
Bournemouth          0.813                -0.65
Brighton             0.777                -0.837
Burnley              0.87                 -0.91
Chelsea              1.349                -0.806
Crystal Palace       0.543                -0.922
Everton              0.899                -0.795
Leicester            1.306                -1.084
Liverpool            1.536                -1.283
Man City             1.721                -1.206
Man United           1.286                -1.216
Newcastle            0.755                -0.766
Norwich              0.391                -0.521
Sheffield United     0.761                -1.163
Southampton          1.052                -0.719
Tottenham            1.218                -0.953
Watford              0.706                -0.669
West Ham             1.014                -0.688
Wolves               1.031                -1.125
------------------------------------------------------------
Home Advantage: 0.229

[5]:

clf.get_params()

[5]:

{'attack_Arsenal': 1.1331236899973345,
 'attack_Aston Villa': 0.8398888104788944,
 'attack_Bournemouth': 0.8130897663316354,
 'attack_Brighton': 0.7765248253257214,
 'attack_Burnley': 0.8703023371771994,
 'attack_Chelsea': 1.3487338919001544,
 'attack_Crystal Palace': 0.5425422408745968,
 'attack_Everton': 0.8994386622804366,
 'attack_Leicester': 1.305683340822444,
 'attack_Liverpool': 1.536142023201564,
 'attack_Man City': 1.721273889006884,
 'attack_Man United': 1.2855301627562425,
 'attack_Newcastle': 0.754538349388783,
 'attack_Norwich': 0.3911250522596789,
 'attack_Sheffield United': 0.7614768317608919,
 'attack_Southampton': 1.0516111981282654,
 'attack_Tottenham': 1.2177895314312077,
 'attack_Watford': 0.7064633632379288,
 'attack_West Ham': 1.0135085594841655,
 'attack_Wolves': 1.0312134741439425,
 'defence_Arsenal': -0.9373593152185443,
 'defence_Aston Villa': -0.6183887167614771,
 'defence_Bournemouth': -0.6497780016893445,
 'defence_Brighton': -0.8366644455845825,
 'defence_Burnley': -0.9096445581199665,
 'defence_Chelsea': -0.8056794617596451,
 'defence_Crystal Palace': -0.9217234584764492,
 'defence_Everton': -0.7950830265711245,
 'defence_Leicester': -1.0841115587982018,
 'defence_Liverpool': -1.283371292184721,
 'defence_Man City': -1.2063235545625164,
 'defence_Man United': -1.2156312104034752,
 'defence_Newcastle': -0.766019714032436,
 'defence_Norwich': -0.520671121600944,
 'defence_Sheffield United': -1.1626929246095525,
 'defence_Southampton': -0.7187488659558111,
 'defence_Tottenham': -0.953348247236947,
 'defence_Watford': -0.6693854533833772,
 'defence_West Ham': -0.6878737389877748,
 'defence_Wolves': -1.1252407856242987,
 'home_advantage': 0.2292519839991387}

Predict Match Outcomes

[6]:

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

[6]:

Module: Penaltyblog

Class: FootballProbabilityGrid

Home Goal Expectation: 1.8967714833577352
Away Goal Expectation: 0.7771220845059058

Home Win: 0.6385321493320355
Draw: 0.21487353680172425
Away Win: 0.1465943119417152

1x2 Probabilities

[7]:

probs.home_draw_away

[7]:

[0.6385321493320355, 0.21487353680172425, 0.1465943119417152]

[8]:

probs.home_win

[8]:

0.6385321493320355

[9]:

probs.draw

[9]:

0.21487353680172425

[10]:

probs.away_win

[10]:

0.1465943119417152

Probablity of Total Goals >1.5

[11]:

probs.total_goals("over", 1.5)

[11]:

0.746563389116105

Probability of Asian Handicap 1.5

[12]:

probs.asian_handicap("home", 1.5)

[12]:

0.38442594605181685

Probability of both teams scoring

[13]:

probs.both_teams_to_score

[13]:

0.45920366069754437