# NBA Lineup Data

Tom Walsh

A preliminary visual investigation of the relationship between the performance of NBA lineups and the players within them.

# Getting NBA Data

The pages at stats.nba.com are backed by a great set of json APIs, making it easy to work with their data. They have an extensive stats for lineups, players, and a lot more.

### Some Libraries We’ll Need

library(rjson)
library(dplyr)

## Getting data from stats.nba.com into R

I used the rjson library to download the json and convert it into an R data frame. The following helper function, given a url, the number of columns, and a list of numeric columns, will fetch the json, convert the data into a matrix, then convert it into a data frame.

df_from_url = function(url, ncol, number_columns) {
json = fromJSON(file = url, method = "C")
df = data.frame(matrix(unlist(json$resultSets[[1]][[3]]), ncol = ncol, byrow = TRUE), stringsAsFactors = FALSE) colnames(df) = json$resultSets[[1]][[2]]
df[, number_columns] = apply(df[, number_columns], 2, function(x) as.numeric(as.character(x)))
return(df)
}

## Some Setup

### Years

The APIs take seasons as strings, so we need to convert from the years in question to the formatted season strings. 2007 is the first year for which they have lineup data.

years = sapply(2007:2015, function(year) sprintf("%4d-%02d", year, (year + 1)%%100))

### Team Ids

This is absolute overkill, because none of the team ids have changed over the year range we’re interested in, but I didn’t know that for sure until after I’d ran it.

team_fmt = "http://stats.nba.com/stats/leaguedashteamstats?Conference=&DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&LastNGames=0&LeagueID=00&Location=&MeasureType=Base&Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&PerMode=Per100Plays&Period=0&PlayerExperience=&PlayerPosition=&PlusMinus=N&Rank=N&Season=%s&SeasonSegment=&SeasonType=Regular+Season&ShotClockRange=&StarterBench=&TeamID=0&VsConference=&VsDivision="
team_urls = sapply(years, function(year) sprintf(team_fmt, year))
team_dfs = sapply(team_urls, function(url) df_from_url(url, 30, c(1, 3:29)))
team_ids = Reduce(union, team_dfs[1, ])

## Player Data

First, we download the player data. We’ll loop over the years and NBA stat collections, and then combine all the data together with merge and rbind into one big data frame. We request stats per 100 plays, but the API seems to intelligently determine when to respect that.

columns = c(35, 32, 24, 27, 30)
stat_types = c("Base", "Advanced", "Misc", "Scoring", "Usage")
player_fmt = "http://stats.nba.com/stats/leaguedashplayerstats?College=&Conference=&Country=&DateFrom=&DateTo=&Division=&DraftPick=&DraftYear=&GameScope=&GameSegment=&Height=&LastNGames=0&LeagueID=00&Location=&MeasureType=%s&Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&PerMode=Per100Plays&Period=0&PlayerExperience=&PlayerPosition=&PlusMinus=N&Rank=N&Season=%s&SeasonSegment=&SeasonType=Regular+Season&ShotClockRange=&StarterBench=&TeamID=0&VsConference=&VsDivision=&Weight="
players = NULL
for (year in years) {
season_df = NULL
for (i in 1:length(stat_types)) {
stat_type = stat_types[i]
c = columns[i]
numeric_columns = c(1, 3, 5:(c - 1))
url = sprintf(player_fmt, stat_type, year)
df = df_from_url(url, c, numeric_columns)
if (is.null(season_df)) {
season_df = df
} else {
season_df = merge(season_df, df, by = 1, all.x = TRUE, suffixes = c("",
sprintf("_%s", stat_type)))
}
}
season_df$SEASON = factor(year) if (is.null(players)) { players = season_df } else { players = rbind(players, season_df) } } ## Lineup Data Fetching lineup data is similar; however, the API is limited to 250 entries per response, so we loop through the years, teams, and stat groups. This results in well over a thousand API calls, and can take a very long time to run. stat_types = c("Base", "Advanced", "Four+Factors", "Misc", "Scoring", "Opponent") columns = c(31, 24, 18, 18, 25, 31) lineup_fmt = "http://stats.nba.com/stats/leaguedashlineups?Conference=&DateFrom=&DateTo=&Division=&GameID=&GameSegment=&GroupQuantity=5&LastNGames=0&LeagueID=00&Location=&MeasureType=%s&Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&PerMode=Per100Plays&Period=0&PlusMinus=N&Rank=N&Season=%s&SeasonSegment=&SeasonType=Regular+Season&ShotClockRange=&TeamID=%d&VsConference=&VsDivision=" lineups = NULL for (year in years) { for (team in team_ids) { season_df = NULL for (i in 1:length(stat_types)) { stat_type = stat_types[i] c = columns[i] numeric_columns = c(4, 6:c) url = sprintf(lineup_fmt, stat_type, year, team) df = df_from_url(url, c, numeric_columns) if (is.null(season_df)) { season_df = df } else { season_df = merge(season_df, df, by = 2, all.x = TRUE, suffixes = c("", sprintf("_%s", stat_type))) } } season_df$SEASON = factor(year)
if (is.null(lineups)) {
lineups = season_df
} else {
lineups = rbind(lineups, season_df)
}
}
}

## Player Cleanup

The API treats minutes differently depending upon the stat group requested. Advanced appears to return minutes per game while Usage returns total minutes. We rename these appropriately.

players = mutate(players, MIN_TOTAL = MIN_Usage, MIN_GAME = MIN_Advanced)
players = select(players, -X, -matches("CFID|CFPARAMS|_[A-Z][a-z]", FALSE))

## Lineup Cleanup

For lineups, however, Advanced seems to return the total minutes. Once again, we rename the column.

lineups = tbl_df(lineups)
lineups = mutate(lineups, MIN_TOTAL = MIN_Advanced)
lineups = select(lineups, -X, -matches("GROUP_SET|CFID|CFPARAMS|_[A-Z][a-z]", FALSE))
lineups = Filter(function(x) !all(is.na(x)), lineups)

## Identifying players

To match the lineup data to the player data, we need to identify the players in each lineup. Parsing the GROUP_ID allows us to do that.

lineups$PLAYERS = t(sapply(lineups$GROUP_ID, function(x) {
as.integer(unlist(strsplit(as.character(x), split = " - ")))
}))

## Calculating Player Averages for a Lineup

For a given lineup, we find the stats for the players in the lineup and average them. For some stats, this makes sense. For others, it won’t. In many cases, we’ll be more interested in the sum, but we can get that later by multiplying by 5.

season_col = grep("SEASON", colnames(lineups))
player_col = grep("PLAYERS", colnames(lineups))
numeric_player_columns = as.vector(which(sapply(players, is.numeric)))
lineup_averages = data.frame(t(apply(lineups, 1, function(x) {
srows = players$SEASON == x[season_col] prows = players$PLAYER_ID %in% as.numeric(x[player_col:player_col + 4])
sapply(players[srows & prows, numeric_player_columns], mean)
})))

## Calculating Usage-Weighted Averages for a Lineup

We do something similar to calculate the usage-weighted averages for a lineup. This won’t make any sense for most stats, but for many offensive stats, it should provide a more reasonable estimate than a straight average.

usg_weighted = data.frame(t(apply(lineups, 1, function(x) {
srows = players$SEASON == x[season_col] prows = players$PLAYER_ID %in% as.numeric(x[player_col:player_col + 4])
stats = players[srows & prows, numeric_player_columns]
tot_usg = sum(stats$USG_PCT_PCT) sapply(stats, function(y) sum(y * stats$USG_PCT_PCT)/tot_usg)
})))

## Putting it all together

Finally, we add suffixes to our lineups, averages, and usage-weighted averages, and merge them all together into a gigantic data frame.

colnames(lineups) = paste(names(lineups), "lineup", sep = ".")
colnames(lineup_averages) = paste(names(lineup_averages), "player", sep = ".")
colnames(usg_weighted) = paste(names(usg_weighted), "usage", sep = ".")
nba = merge(merge(lineups, lineup_averages, by = 0), usg_weighted, by = 0)
nba$Row.names = NULL nba$Row.names = NULL
dim(nba)
## [1] 66784   259

## Net Rating

In the end, what we really care about is the Net Rating of lineups. Will our lineup score more points than their opponents? It’s important to note stats.nba.com formulation of Net Rating (and hence Offensive Rating and Defensive Rating) for players is essentially scaled +/-, and is distinct from the Dean Oliver version of these stats.

It seems that the volume of 3 pointers made by a lineup doesn’t really depend upon the volume of 3 pointers taken by the players in that lineup. It may be that this is far more dependent on strategy, but this definitely needs more investigation.

### Tom Walsh

Tom Walsh (M.Sc. Computer Science, University of Toronto) developed a desire to get deeper into the data while leading a team of developers at BSports building Scouting Information Systems for Major League Baseball teams. A course on Basketball...
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