url<-url("https://github.com/pjournal/mef03-karaahmetlid/blob/master/atp_tennis_data_2017.RData?raw=true")
load(url)

library(tidyverse)
library(dplyr)
library(ggplot2)
library(viridis) 
library(knitr)

Task1: Rank countries by the singles champions.

player_tourney_df <- left_join(tourney_df, player_df, by = c("singles_winner_player_id" = "player_id"))
player_tourney_df %>% select(flag_code) %>% group_by(flag_code) %>% summarise(Count=n()) %>% arrange(desc(Count))

## # A tibble: 21 x 2
##    flag_code Count
##    <chr>     <int>
##  1 ESP          11
##  2 USA           9
##  3 SUI           8
##  4 FRA           7
##  5 GER           7
##  6 BUL           4
##  7 ARG           2
##  8 BEL           2
##  9 BIH           2
## 10 CRO           2
## # ... with 11 more rows

Task 2: Rank countries which did not get any singles championships by the games won when they win the match.

non_ch_pl <- player_df %>% select(player_id, flag_code) %>% anti_join(player_tourney_df)
country_non_ch_pl <- non_ch_pl %>% left_join(score_df, by = c("player_id" = "winner_player_id")) %>% 
  group_by(flag_code) %>% summarise(totalWon = sum(winner_games_won, na.rm = TRUE)) %>% arrange(desc(totalWon))
country_non_ch_pl

## # A tibble: 93 x 2
##    flag_code totalWon
##    <chr>        <dbl>
##  1 AUS           1989
##  2 CZE           1209
##  3 CAN           1190
##  4 SVK            889
##  5 BRA            873
##  6 POR            621
##  7 RSA            566
##  8 KAZ            495
##  9 KOR            438
## 10 GEO            377
## # ... with 83 more rows

country_non_ch_pl %>% top_n(20) %>%
  ggplot(aes(x = flag_code, y = totalWon, fill = flag_code)) +
  geom_col()

Time spent on court of the players who had reached at least Quarter Finals in Grand Slam Tournements

match_dur_df <- left_join(score_df, stats_df) %>% 
  select("match_id", "tourney_id", "winner_player_id", "loser_player_id", "tourney_round_name", "match_duration")
match_dur_df$tourney_id <- as.numeric(match_dur_df$tourney_id)
match_dur_df

## # A tibble: 3,834 x 6
##    match_id tourney_id winner_player_id loser_player_id tourney_round_n~
##    <chr>         <dbl> <chr>            <chr>           <chr>           
##  1 2017-33~        339 d875             n552            Finals          
##  2 2017-33~        339 d875             r975            Semi-Finals     
##  3 2017-33~        339 n552             w367            Semi-Finals     
##  4 2017-33~        339 r975             n409            Quarter-Finals  
##  5 2017-33~        339 w367             e831            Quarter-Finals  
##  6 2017-33~        339 n552             tc61            Quarter-Finals  
##  7 2017-33~        339 d875             tb69            Quarter-Finals  
##  8 2017-33~        339 r975             sm37            Round of 16     
##  9 2017-33~        339 w367             t840            Round of 16     
## 10 2017-33~        339 n552             dc58            Round of 16     
## # ... with 3,824 more rows, and 1 more variable: match_duration <dbl>

tourney_GSlam <- tourney_df %>% 
  filter(tourney_singles_draw == 128) %>% select("tourney_id", "tourney_name")
tourney_GSlam

## # A tibble: 4 x 2
##   tourney_id tourney_name   
##        <dbl> <chr>          
## 1        580 Australian Open
## 2        520 Roland Garros  
## 3        540 Wimbledon      
## 4        560 US Open

GSlam_Mdur <- left_join(tourney_GSlam, match_dur_df)
kable(head(GSlam_Mdur, n = 20L))

tourney_id	tourney_name	match_id	winner_player_id	loser_player_id	tourney_round_name	match_duration
580	Australian Open	2017-580-f324-n409	f324	n409	Finals	218
580	Australian Open	2017-580-f324-w367	f324	w367	Semi-Finals	185
580	Australian Open	2017-580-n409-d875	n409	d875	Semi-Finals	296
580	Australian Open	2017-580-n409-r975	n409	r975	Quarter-Finals	164
580	Australian Open	2017-580-w367-t786	w367	t786	Quarter-Finals	135
580	Australian Open	2017-580-d875-gb88	d875	gb88	Quarter-Finals	133
580	Australian Open	2017-580-f324-z168	f324	z168	Quarter-Finals	92
580	Australian Open	2017-580-z168-mc10	z168	mc10	Round of 16	214
580	Australian Open	2017-580-r975-bd06	r975	bd06	Round of 16	169
580	Australian Open	2017-580-w367-sa93	w367	sa93	Round of 16	164
580	Australian Open	2017-580-f324-n552	f324	n552	Round of 16	204
580	Australian Open	2017-580-n409-mc65	n409	mc65	Round of 16	176
580	Australian Open	2017-580-gb88-tb69	gb88	tb69	Round of 16	164
580	Australian Open	2017-580-t786-e687	t786	e687	Round of 16	173
580	Australian Open	2017-580-d875-i165	d875	i165	Round of 16	145
580	Australian Open	2017-580-mc10-q927	mc10	q927	Round of 32	119
580	Australian Open	2017-580-r975-sd32	r975	sd32	Round of 32	155
580	Australian Open	2017-580-w367-t840	w367	t840	Round of 32	152
580	Australian Open	2017-580-n552-l797	n552	l797	Round of 32	131
580	Australian Open	2017-580-mc65-k435	mc65	k435	Round of 32	116

GSlam_QF_Players <- GSlam_Mdur %>% filter(tourney_round_name == "Round of 16") %>% 
  select("winner_player_id")  %>% distinct(winner_player_id) %>% 
  left_join(player_df,  by = c("winner_player_id" = "player_id")) %>%
  select(1, 2, 3, 4)
kable(GSlam_QF_Players)

winner_player_id	player_slug	first_name	last_name
z168	mischa-zverev	Mischa	Zverev
r975	milos-raonic	Milos	Raonic
w367	stan-wawrinka	Stan	Wawrinka
f324	roger-federer	Roger	Federer
n409	rafael-nadal	Rafael	Nadal
gb88	david-goffin	David	Goffin
t786	jo-wilfried-tsonga	Jo-Wilfried	Tsonga
d875	grigor-dimitrov	Grigor	Dimitrov
mc10	andy-murray	Andy	Murray
d643	novak-djokovic	Novak	Djokovic
cd85	pablo-carreno-busta	Pablo	Carreno Busta
tb69	dominic-thiem	Dominic	Thiem
c977	marin-cilic	Marin	Cilic
n552	kei-nishikori	Kei	Nishikori
ma30	gilles-muller	Gilles	Muller
ba47	tomas-berdych	Tomas	Berdych
q927	sam-querrey	Sam	Querrey
d683	juan-martin-del-potro	Juan Martin	del Potro
re44	andrey-rublev	Andrey	Rublev
sm37	diego-schwartzman	Diego	Schwartzman
a678	kevin-anderson	Kevin	Anderson

min_on_court <- GSlam_Mdur %>% 
  group_by(winner_player_id) %>% summarise(player_on_court = sum(match_duration))
min_on_court <- left_join(GSlam_QF_Players, min_on_court) %>% select(4, 5) %>% na.omit() %>%
  arrange(desc(player_on_court))
kable(min_on_court)

last_name	player_on_court
Nadal	3235
Federer	2295
Thiem	1885
Anderson	1856
Rublev	1762
Murray	1706
Wawrinka	1686
Cilic	1675
Carreno Busta	1530
Zverev	1520
Raonic	1440
Nishikori	1422
Dimitrov	1302
Goffin	1270
del Potro	1182
Djokovic	1162
Berdych	1085
Muller	1055
Schwartzman	1020
Tsonga	944

min_on_court %>%
  ggplot(aes(x = last_name, y = player_on_court, fill = last_name)) +
  geom_col() + 
  scale_fill_viridis(discrete=TRUE) +
  theme(legend.position = "bottom")

Point win/loss ratios of the players ranked top 5 by year end 2017

top5_players <- rank_df %>% filter(week_title == max(week_title)) %>% top_n(5, ranking_points) %>%
  select(2, 3) %>% left_join(select(player_df, player_id, first_name, last_name))
kable(top5_players)

player_id	rank_number	first_name	last_name
n409	1	Rafael	Nadal
f324	2	Roger	Federer
d875	3	Grigor	Dimitrov
z355	4	Alexander	Zverev
tb69	5	Dominic	Thiem

w_pts_win_loss <- stats_df %>% 
  select("winner_player_id", "winner_total_points_won", "loser_total_points_won") %>% na.omit() %>%
  group_by(winner_player_id) %>% 
  summarise(w_tot_pts_won = sum(winner_total_points_won), w_tot_pts_lost = sum(loser_total_points_won)) 

l_pts_win_loss <- stats_df %>% 
  select("loser_player_id", "loser_total_points_won", "winner_total_points_won") %>% na.omit() %>%
  group_by(loser_player_id) %>% 
  summarise(l_tot_pts_won = sum(loser_total_points_won), l_tot_pts_lost = sum(winner_total_points_won))

winloss_ratio <- left_join(w_pts_win_loss, l_pts_win_loss, by = c("winner_player_id" = "loser_player_id")) %>%
  rename(player_id = winner_player_id) %>%
  mutate(w_l_ratio = (w_tot_pts_won + l_tot_pts_won) / (w_tot_pts_lost + l_tot_pts_lost))
kable(head(winloss_ratio, n = 12L))

player_id	w_tot_pts_won	w_tot_pts_lost	l_tot_pts_won	l_tot_pts_lost	w_l_ratio
a479	1015	869	712	842	1.0093513
a622	193	160	199	252	0.9514563
a678	3210	2602	1590	1754	1.1019284
a829	1657	1461	1805	2126	0.9651519
a853	62	30	334	436	0.8497854
a887	538	460	366	427	1.0191657
ae14	402	361	309	404	0.9294118
af32	70	64	49	67	0.9083969
b747	1746	1467	1283	1490	1.0243490
b837	1450	1204	1026	1284	0.9951768
b884	1275	1052	1333	1620	0.9760479
b896	514	441	800	906	0.9755011

top5_wlR <- left_join(top5_players, winloss_ratio)
kable(top5_wlR)

player_id	rank_number	first_name	last_name	w_tot_pts_won	w_tot_pts_lost	l_tot_pts_won	l_tot_pts_lost	w_l_ratio
n409	1	Rafael	Nadal	5572	4158	947	1066	1.247894
f324	2	Roger	Federer	4686	3754	467	476	1.218203
d875	3	Grigor	Dimitrov	4110	3277	1685	1862	1.127651
z355	4	Alexander	Zverev	4418	3701	1677	1939	1.080674
tb69	5	Dominic	Thiem	4053	3286	2119	2468	1.072645

ATP 2017

Dost Karaahmetli

December 3, 2019

Task1: Rank countries by the singles champions.

Task 2: Rank countries which did not get any singles championships by the games won when they win the match.

Time spent on court of the players who had reached at least Quarter Finals in Grand Slam Tournements

Point win/loss ratios of the players ranked top 5 by year end 2017