Code for this analysis is availabile on Github
I plan on using this space to post my musings on statistics that, shall we say, fall outside of the hallowed halls of academia. For the most part, this will mean lots of R, and lots of hockey and Twitter data. I hope to share something interesting every few weeks, though with my ultra hectic (wink) grad student life, we’ll see how long I can keep that promise…
For now here’s a few snippets of code to show I can make my way around R.
A Quick Example - Working with Hockey Data
Say we were interested in figuring out how many goals were scored against the Montreal Canadiens in a given season. You see, the thought in Montreal is that players from the province of Quebec seem to elevate their play when facing their hometown team. Here I’ll demonstrate some of the data wrangling needed to investigate this particular problem (though perhaps there is more to this question to think about for the future…)
Let’s start by loading a few handy libraries.
library(XML) # Web scraping library(dplyr) # Lifeblood of data management library(lubridate) # For fixing dates
First we require a list of goals scored against the Canadiens. This can be obtained using the
nhlscrapr package (available from CRAN). In the interest of time, I will focus here on the 2014-2015 NHL season. Why 2014-2015? Because I really want to get a decent test page online? (Really, it’s the most recent season for which near-complete data is available, last I checked anyway, and where the reference dates work properly.)
The loaded data is split across three individual datasets. We will make use of
nhlscrapr.20142015.pbp. The play-by-play dataset has as each row every individual event logged by the NHL in their official game sheets for that season, such as goals, shots, penalties, and the like, along with information about each event, such as the player directly involved, his team, the home and away teams, period, etc. The roster dataset has for each row the name and some identifying information (though not the birthplace!) for each player who played at least one game in the given season.
For the roster, we require only the first and last name of the player, along with his identifying code in the play-by-play data. For the play-by-play, we first filter out all non-goal events and all shootout goals (which is marked as occurring in period 5). We then merge in the player name based on the identifying code. The game date is obtained as the number of days elapsed since January 1, 2002. We also extract the team that was scored on, and, because it might be interesting, whether or not the game occurred in Montreal. Finally, we retain only the goals where Montreal (“MTL”) is specified as the opposing team.
# Get player names and identifying codes roster <- nhlscrapr.20142015.roster %>% select(firstlast,index) %>% rename(Player = firstlast,PlayerCode=index) # Retrieve all goals scored against Montreal goals <- nhlscrapr.20142015.pbp %>% filter(etype=="GOAL", period <= 4) %>% # Consider only non-shootout goals select(ev.team,ev.player.1,awayteam,hometeam,refdate) %>% rename(Team=ev.team,PlayerCode=ev.player.1,Away=awayteam,Home=hometeam,Date=refdate) %>% inner_join(roster,by="PlayerCode") %>% # Merge in player names mutate(Date = ymd("2002-01-01") + days(Date)) %>% # Get correct date select(-PlayerCode) %>% rowwise() %>% mutate(Opponent = ifelse(Team==Away,Home,Away), # Get opponent of team that scored InMTL = Home=="MTL") %>% # Did the game occur in Montreal? T/F select(-Home,-Away) %>% filter(Opponent == "MTL") %>% # Consider only goals against Montreal ungroup() %>% arrange(Date)
## Team Date Player Opponent InMTL ## 1 TOR 2014-10-08 NAZEM KADRI MTL FALSE ## 2 TOR 2014-10-08 TYLER BOZAK MTL FALSE ## 3 TOR 2014-10-08 MORGAN RIELLY MTL FALSE ## 4 WSH 2014-10-09 ANDRE BURAKOVSKY MTL FALSE ## 5 PHI 2014-10-11 MICHAEL RAFFL MTL FALSE ## 6 PHI 2014-10-11 WAYNE SIMMONDS MTL FALSE
Some of you may have noticed I use capital letters for my column names. Hopefully my blatant disregard for standard convention doesn’t offend anyone too much (or my frequent use of ellipses for that matter…)
To get the player birthplaces, I will cross-reference the above information with a list of birthplaces for NHL players. Conveniently, Hockey-Reference has tables of players birthplaces divided by region (listed under the “frivolities” section). The table for players born in Quebec can be found here. As a straightforward HTML page, this information can be easily retrieved using the
readHTMLTable function from the XML package.
url <- c("http://www.hockey-reference.com/friv/birthplaces.cgi?country=CA&province=QC&state=") # Only 1 Table in list, column 12 repeats GP for goaltenders, remove it as the column name is the same as the previous and causes issues quebec.players <- readHTMLTable(url,stringsAsFactors=F)[][,-12] %>% filter(Player != "Player") # Remove rows not corresponding to any player names(quebec.players) <- c("Rk","Player","Start.Year","End.Date", "Position","GP","G","A","PTS","Plus-Minus", "PIM","W","L","TO","SV","GAA","Birthplace","Birth.Date","Death.Date")
Since we’re only interested in goal scorers, we can remove goalies, and since we’re only observing the 2014-2015 season here, we can exclude all players who retired prior to 2014. Luckily, here, there is no need for much string editing; a simple conversion of the player names in this dataset to upper case will match up names with columns from the
relevant.quebec.players <- quebec.players %>% mutate(End.Date = as.numeric(End.Date)) %>% filter(End.Date >= 2014, Position != "G") %>% # Consider only shooters who played up to to 2014-2015 season mutate(Player = toupper(Player)) %>% select(Player,Birthplace) head(relevant.quebec.players)
## Player Birthplace ## 1 MARK BARBERIO Montreal ## 2 FRANCOIS BEAUCHEMIN Sorel ## 3 ANTHONY BEAUVILLIER Sorel-Tracy ## 4 PATRICE BERGERON Ancienne-Lorette ## 5 STEVE BERNIER Quebec City ## 6 ALEX BIEGA Montreal
Now it’s a simple matter of merging the two tables. The inner join will make it such that only values appearing in both tables will be included in the final table, thus stripping our table down to only those Quebecois players with goals against the Habs that year.
quebec.against.mtl <- inner_join(goals,relevant.quebec.players,by="Player") %>% select(Team,Date,Player,InMTL,Birthplace) quebec.against.mtl
## Team Date Player InMTL Birthplace ## 1 BOS 2014-10-16 SIMON GAGNE TRUE Ste. Foy ## 2 COL 2014-10-18 ALEX TANGUAY TRUE Ste-Justine ## 3 MIN 2014-11-08 JASON POMINVILLE TRUE Repentigny ## 4 NYR 2014-11-23 MARTIN ST. LOUIS FALSE Laval ## 5 COL 2014-12-01 DANIEL BRIERE FALSE Gatineau ## 6 MIN 2014-12-03 JASON POMINVILLE FALSE Repentigny ## 7 PIT 2015-01-03 DAVID PERRON FALSE Sherbrooke ## 8 NSH 2015-01-20 MIKE RIBEIRO TRUE Montreal ## 9 ANA 2015-03-04 FRANCOIS BEAUCHEMIN FALSE Sorel ## 10 T.B 2015-03-30 JONATHAN DROUIN TRUE Ste-Agathe
This was all pretty easy. The final tally appears to be 10 goals by Quebecers, with 5 occurring in Montreal. This seems… well, quite low! I’ll note that this doesn’t take into account francophone players not born in the province of Quebec (Claude Giroux, for example, is from Ontario).
So is this what we should expect from Quebec born players in games against Montreal? Or do francophone players play better in their hometown? Admittedly, this is a very surface-level impression of the problem. Let’s think about this a little more… next time ;)