forked from jtattersall09403/BluffBall
-
Notifications
You must be signed in to change notification settings - Fork 0
/
startprob.R
257 lines (218 loc) · 10.1 KB
/
startprob.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
# ------------------------------ Predicting probability of starting ------------------------------ #
library(dplyr)
library(reshape2)
library(TTR)
library(parallel)
library(fplr)
library(RcppRoll)
library(pROC)
library(purrr)
source('./GetFPLData.R')
# Get player and team data
data <- fpl %>%
mutate(id = as.numeric(id))
# Geom series
geomSeries <- function(base, n) {
base^(1:n)
}
# Get teams data
teams.2 = teams()
players.2 = players()
# Define function to get all detailed player data
details <- function(id, plyrs) {
tryCatch({
# get player details
x = playerDetailed2(id, plyrs) %>%
inner_join(dplyr::select(players.2, id, web_name, position, team_name), by = c("player_id"="id")) %>%
inner_join(dplyr::select(teams.2, name, strength_overall_home:strength_defence_away), by = c("team_name"="name")) %>%
inner_join(dplyr::select(teams.2, name, strength_overall_home:strength_defence_away), by = c("opponent_team"="name")) %>%
mutate(team_attack_strength = ifelse(was_home, strength_attack_home.x, strength_attack_away.x),
opp_defence_strength = ifelse(was_home, strength_defence_away.y, strength_defence_home.y),
strength_ratio = team_attack_strength/opp_defence_strength,
position = as.factor(position)) %>%
group_by(player_id) %>%
arrange(round) %>%
mutate(assists_act = assists,
strength_ratio_act = strength_ratio)
# Change time to time object
x <- x %>%
mutate(kickoff_time = as.Date(kickoff_time),
minutes = as.integer(minutes),
total_points = as.integer(total_points))
# Set player id
x$id = x$player_id
# Binary transferred in/out
x = x %>%
mutate(trans_in = ifelse(transfers_balance < 0, 0, 1))
# Convert to time series
xt = xts::xts(x, order.by = x$kickoff_time)
# Get weighted moving average of minutes and points
n <- ifelse(nrow(x) < 5, nrow(x), 5)
x$avmins <- WMA(x$minutes, n=n, w = geomSeries(1.3, n))
x$form2 <- WMA(x$total_points, n=n, w = geomSeries(1.3, n))
x <- x %>%
mutate(assists = assists/strength_ratio,
assists_5 = roll_mean(assists, n, align = "right", fill = NA),
crosses_5 = roll_mean(open_play_crosses/strength_ratio, n, align = "right", fill = NA),
bigchance_5 = roll_mean(big_chances_created/strength_ratio, n, align = "right", fill = NA),
keypass_5 = roll_mean(key_passes/strength_ratio, n, align = "right", fill = NA),
influence_5 = roll_mean(influence/strength_ratio, n, align = "right", fill = NA),
creativity_5 = roll_mean(creativity/strength_ratio, n, align = "right", fill = NA),
threat_5 = roll_mean(threat/strength_ratio, n, align = "right", fill = NA),
ict_index_5 = roll_mean(ict_index/strength_ratio, n, align = "right", fill = NA))
# Convert back to data frame
x = as.data.frame(x)
x = x %>%
mutate(avmins_lag = lag(avmins, 1)) %>%
mutate(assists_any = ifelse(assists_act > 0, 1, 0)) %>%
dplyr::select(id, kickoff_time, minutes, trans_in, total_points, avmins, avmins_lag, form2,
round, fixture, web_name, position, team_name, opponent_team,
strength_ratio_act, team_attack_strength, opp_defence_strength, influence_5, creativity_5, threat_5, ict_index_5,
assists_5, crosses_5, bigchance_5, keypass_5, assists_any, assists_act)
# Return data
return(x)
}, error = function(e) {
return(NA)
}, finally = print(paste0(100*round(id/max(data$id),3), '% processed'))
)
}
# Calculate the number of cores
# no_cores <- detectCores() - 1
#
# # Initiate cluster
# cl <- makeCluster(no_cores)
#
# # Include relevant variables and packages
# clusterEvalQ(cl, {
# library(jsonlite)
# library(dplyr)
# library(ggplot2)
# library(ggrepel)
# library(engsoccerdata)
# library(devtools)
# library(fplr)
# library(dplyr)
# library(reshape2)
# library(TTR)
# library(RcppRoll)
# geomSeries <- function(base, n) {
# base^(1:n)
# }
# })
#
# clusterExport(cl, c("data", "players.2", "teams.2"))
# Get all data for modelling. Now only takes around 1 minute!
#modeldata <- parLapply(cl, sort(unique(data$id)), details)
plyrs <- jsonlite::read_json("https://fantasy.premierleague.com/drf/bootstrap-static", simplifyVector = TRUE)
modeldata <- lapply(sort(data$id), details, plyrs=plyrs)
# Close cluster
# stopCluster(cl)
modeldata2 <- modeldata[!is.na(modeldata)]
# Prepare data for modelling. Create binary 60+ mins variable, and scale assist predictors within rounds
modeldata3 <- do.call(rbind, modeldata2) %>%
filter(!is.na(avmins)) %>%
mutate(mins60 = ifelse(minutes >= 60, 1, 0),
minsany = ifelse(minutes >0, 1, 0),
trans_in = as.factor(trans_in)) %>%
group_by(team_name, round) %>%
mutate(av_threat = mean(threat_5, na.rm = TRUE),
max_threat = max(threat_5, na.rm = TRUE)) %>%
group_by(round) %>%
mutate_at(vars(influence_5, creativity_5, threat_5, ict_index_5,
assists_5, crosses_5, bigchance_5, keypass_5), function(x) as.numeric(scale(x)))
# View(arrange(modeldata3, team_name, round))
saveRDS(modeldata3, './Project files/startprob_modeldata.rds')
# ------------------------------ Train models ------------------------------ #
# Quick visualisation
# modeldata3 %>%
# sample_n(size = 1000) %>%
# ggplot(aes(x = avmins, y = mins60)) +
# geom_point(color = 'dodgerblue4', alpha = 0.5)
# Create training and test data
n <- round(nrow(modeldata3)*2/3,0)
train<- modeldata3[1:n,]
test <- modeldata3[n+1:nrow(modeldata3),]
model <- glm(mins60 ~ avmins_lag + trans_in, family=binomial(link='logit'),data=train)
summary(model)
# Evaluate model
fitted.results <- predict(model,newdata=test,type='response')
test$pred <- fitted.results
test %>% select(minutes, pred) %>% View
fitted.results <- ifelse(fitted.results > 0.5,1,0)
misClasificError <- mean(fitted.results != test$mins60, na.rm = T)
print(paste('Accuracy',1-misClasificError))
print(psych::cohen.kappa(table(fitted.results, test$mins60)))
print(roc(fitted.results, test$mins60))
# Save model
saveRDS(model, './startprob.rds')
# model <- readRDS('./startprob.rds')
# ------------------------------ Produce probability of starting next game ----------------------------- #
# Get most recent row for each player from detail table
modelresults <- modeldata3 %>%
group_by(id) %>%
mutate(rank = rank(desc(kickoff_time))) %>%
filter(rank == 1) %>%
mutate(avmins_lag = avmins) %>%
select(id, avmins_lag, trans_in, form2) %>%
ungroup
# Match weighted average minutes and form to fpl data, and rename variables
# Predict next starting probability. Set to zero if injured.
fpl.1 <- fpl %>%
mutate(id = as.numeric(id)) %>%
left_join(modelresults, by = 'id')
fpl.1$prob60 <- ifelse(fpl.1$status == 'a', predict(model, newdata = fpl.1, type = 'response'), 0)
fpl.1 <- fpl.1 %>%
mutate(prob60 = ifelse(prob60 < .15, 0, prob60))
# ------------ Predict assists -----------------------------------------------------
# ------------ Deprecated until we have enough data to predict it accurately -------
#
# model2 <- readRDS('gbm_assist_model.RDS')
#
# # Get most recent row for each player from detail table, and get next fixture team strengths
# as_modeldata <- modeldata3 %>%
# group_by(id) %>%
# mutate(rank = rank(desc(kickoff_time))) %>%
# filter(rank == 1) %>%
# mutate(avmins_lag = avmins) %>%
# left_join(select(fix2,-fixture), by = "team_name") %>%
# inner_join(dplyr::select(teams.2, name, strength_overall_home:strength_defence_away), by = c("team_name"="name")) %>%
# inner_join(dplyr::select(teams.2, name, strength_overall_home:strength_defence_away), by = c("opponent_team.y"="name")) %>%
# mutate(team_attack_strength = ifelse(was_home, strength_attack_home.x, strength_attack_away.x),
# opp_defence_strength = ifelse(was_home, strength_defence_away.y, strength_defence_home.y),
# strength_ratio = team_attack_strength/opp_defence_strength,
# position = as.factor(position)) %>%
# select(round, fixture, order_fix, num_fix, id, web_name, position, team_name, opponent_team.y,
# strength_ratio_act=strength_ratio, team_attack_strength,opp_defence_strength, influence_5, creativity_5, threat_5, ict_index_5,
# assists_5, crosses_5, bigchance_5, keypass_5, av_threat, max_threat, assists_any, assists_act) %>%
# ungroup
#
# # Prepare data
# as_modeldata.2 <- as_modeldata[complete.cases(as_modeldata),] %>%
# filter(position != 'Goalkeeper')
#
# # Get predictions
# p_as <- predict(model2, newdata=as_modeldata.2, type = "prob")
#
# as_modelresults <- cbind(as_modeldata.2, p_as) %>%
# mutate(xpas = ((OneAssist+TwoAssists+ThreeAssists)*3)) %>%
# mutate(probas = OneAssist+TwoAssists+ThreeAssists) %>%
# dplyr::select(id, team_attack_strength, opp_defence_strength, order_fix, num_fix, probas, xpas)
# fpl.1 <- fpl.1 %>%
# left_join(as_modelresults, by = c('id','order_fix')) %>%
# mutate_at(vars(probas, xpas), function(x) ifelse(is.na(x), 0,
# ifelse(.$status != 'a', 0, x)))
# Get upcoming attack:defence strength ratios for weighting assist probability
strengthratios <- fpl.1 %>%
inner_join(dplyr::select(teams.2, name, strength_overall_home:strength_defence_away), by = c("team"="name")) %>%
inner_join(dplyr::select(teams.2, name, strength_overall_home:strength_defence_away), by = c("opponent_team"="name")) %>%
mutate(team_attack_strength = ifelse(was_home, strength_attack_home.x, strength_attack_away.x),
opp_defence_strength = ifelse(was_home, strength_defence_away.y, strength_defence_home.y),
strength_ratio = team_attack_strength/opp_defence_strength) %>%
select(team, strength_ratio, team_attack_strength, opp_defence_strength) %>%
unique
# Get simple assist prediction. Effectively just for tie-breaking.
fpl.1 <- fpl.1 %>%
left_join(strengthratios, by = "team") %>%
mutate(probas = ifelse(!team %in% fix2$team_name, 0, prob60 * 90*assists/minutes),
probas = probas * strength_ratio) %>%
arrange(desc(probas))