library(tidyverse)
library(nbastatR)
library(lubridate)
library(gt)
library(skimr)The bref_team_stats() function in nbastatR will, by default, assign several data frames to the environment: dataBREFShootingTeams, dataBREFStandings, dataBREFStandingsConfTeams, dataBREFStandingsDivTeams, dataBREFTeamJoined, and dataBREFTotalsTeams. All of these are summary tables for the teams over the course of the season.
bref_teams_stats(seasons = 2019)
#> Parsing https://www.basketball-reference.com/leagues/NBA_2019.html
#> StandingsConf
#> Assigning NBA player dictionary to df_dict_nba_players to your environment
#> StandingsDiv
#> PerGame
#> Totals
#> PerPoss
#> Misc
#> Shooting
#> # A tibble: 1 x 2
#> nameTable dataTable
#> <chr> <list>
#> 1 Team Data <tibble [30 × 228]>Team Totals
Here I’ll use the glimpse() function from the tibble package to see what variables are in the data frame.
glimpse(dataBREFTotalsTeams)
#> Observations: 30
#> Variables: 54
#> $ slugSeason <chr> "2018-19", "2018-19", "2018-19", "201…
#> $ yearSeason <dbl> 2019, 2019, 2019, 2019, 2019, 2019, 2…
#> $ urlSeasonBREF <chr> "https://www.basketball-reference.com…
#> $ nameTeam <chr> "Atlanta Hawks", "Boston Celtics", "B…
#> $ isPlayoffTeam <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FAL…
#> $ countGamesTeam <dbl> 80, 80, 80, 79, 80, 80, 79, 79, 79, 7…
#> $ slugTeamBREF <chr> "ATL", "BOS", "BRK", "CHO", "CHI", "C…
#> $ urlBREFTeamData <chr> "https://www.basketball-reference.com…
#> $ idTeamNBA <int> 1610612737, 1610612738, 1610612751, 1…
#> $ urlThumbnailTeam <chr> "https://stats.nba.com/media/img/team…
#> $ minutesTotalsTeam <dbl> 19375, 19300, 19500, 19110, 19425, 19…
#> $ minutesTotalsOpponent <dbl> 19375, 19300, 19500, 19110, 19425, 19…
#> $ fgmTotalsTeam <dbl> 3309, 3364, 3218, 3173, 3191, 3118, 3…
#> $ fgmTotalsOpponent <dbl> 3423, 3157, 3381, 3314, 3375, 3451, 3…
#> $ fgaTotalsTeam <dbl> 7321, 7235, 7156, 7118, 7027, 7016, 6…
#> $ fgaTotalsOpponent <dbl> 7228, 7035, 7398, 7042, 7130, 6988, 7…
#> $ pctFGTotalsTeam <dbl> 0.452, 0.465, 0.450, 0.446, 0.454, 0.…
#> $ pctFGTotalsOpponent <dbl> 0.474, 0.449, 0.457, 0.471, 0.473, 0.…
#> $ fg3mTotalsTeam <dbl> 1033, 1005, 1015, 937, 729, 828, 983,…
#> $ fg3mTotalsOpponent <dbl> 992, 918, 830, 960, 964, 953, 878, 85…
#> $ fg3aTotalsTeam <dbl> 2948, 2754, 2871, 2679, 2077, 2329, 2…
#> $ fg3aTotalsOpponent <dbl> 2749, 2676, 2418, 2647, 2643, 2524, 2…
#> $ pctFG3TotalsTeam <dbl> 0.350, 0.365, 0.354, 0.350, 0.351, 0.…
#> $ pctFG3TotalsOpponent <dbl> 0.361, 0.343, 0.343, 0.363, 0.365, 0.…
#> $ fg2mTotalsTeam <dbl> 2276, 2359, 2203, 2236, 2462, 2290, 2…
#> $ fg2mTotalsOpponent <dbl> 2431, 2239, 2551, 2354, 2411, 2498, 2…
#> $ fg2aTotalsTeam <dbl> 4373, 4481, 4285, 4439, 4950, 4687, 3…
#> $ fg2aTotalsOpponent <dbl> 4479, 4359, 4980, 4395, 4487, 4464, 4…
#> $ pctFG2TotalsTeam <dbl> 0.520, 0.526, 0.514, 0.504, 0.497, 0.…
#> $ pctFG2TotalsOpponent <dbl> 0.543, 0.514, 0.512, 0.536, 0.537, 0.…
#> $ ftmTotalsTeam <dbl> 1402, 1259, 1532, 1456, 1299, 1316, 1…
#> $ ftmTotalsOpponent <dbl> 1700, 1394, 1428, 1273, 1360, 1264, 1…
#> $ ftaTotalsTeam <dbl> 1861, 1561, 2059, 1828, 1651, 1659, 2…
#> $ ftaTotalsOpponent <dbl> 2248, 1832, 1850, 1617, 1777, 1641, 1…
#> $ pctFTTotalsTeam <dbl> 0.753, 0.807, 0.744, 0.796, 0.787, 0.…
#> $ pctFTTotalsOpponent <dbl> 0.756, 0.761, 0.772, 0.787, 0.765, 0.…
#> $ orbTotalsTeam <dbl> 924, 785, 863, 797, 703, 855, 796, 94…
#> $ orbTotalsOpponent <dbl> 847, 828, 889, 784, 804, 766, 813, 76…
#> $ drbTotalsTeam <dbl> 2747, 2779, 2836, 2682, 2725, 2567, 2…
#> $ drbTotalsOpponent <dbl> 2824, 2833, 2815, 2837, 2896, 2765, 2…
#> $ trbTotalsTeam <dbl> 3671, 3564, 3699, 3479, 3428, 3422, 3…
#> $ trbTotalsOpponent <dbl> 3671, 3661, 3704, 3621, 3700, 3531, 3…
#> $ astTotalsTeam <dbl> 2064, 2100, 1902, 1826, 1761, 1658, 1…
#> $ astTotalsOpponent <dbl> 2161, 1883, 1895, 2065, 2047, 2045, 1…
#> $ stlTotalsTeam <dbl> 668, 687, 525, 575, 592, 517, 518, 61…
#> $ stlTotalsOpponent <dbl> 790, 545, 623, 554, 596, 550, 620, 59…
#> $ blkTotalsTeam <dbl> 409, 428, 332, 391, 344, 192, 336, 35…
#> $ blkTotalsOpponent <dbl> 431, 307, 425, 481, 453, 447, 356, 39…
#> $ tovTotalsTeam <dbl> 1369, 1032, 1208, 976, 1144, 1074, 11…
#> $ tovTotalsOpponent <dbl> 1212, 1210, 1076, 1080, 1076, 994, 10…
#> $ pfTotalsTeam <dbl> 1879, 1634, 1737, 1510, 1632, 1608, 1…
#> $ pfTotalsOpponent <dbl> 1766, 1570, 1777, 1631, 1507, 1561, 1…
#> $ ptsTotalsTeam <dbl> 9053, 8992, 8983, 8739, 8410, 8380, 8…
#> $ ptsTotalsOpponent <dbl> 9538, 8626, 9020, 8861, 9074, 9119, 8…Team Per-Game Summaries
To make these summaries a bit more visual, I like to use the skim() function from the skimr package.
skimr::skim(dataBREFPerGameTeams) %>% skimr::kable()
#> Skim summary statistics
#> n obs: 30
#> n variables: 54
#>
#> Variable type: character
#>
#> variable missing complete n min max empty n_unique
#> ------------------ --------- ---------- ---- ----- ----- ------- ----------
#> nameTeam 0 30 30 9 22 0 30
#> slugSeason 0 30 30 7 7 0 1
#> slugTeamBREF 0 30 30 3 3 0 30
#> urlBREFTeamData 0 30 30 56 56 0 30
#> urlSeasonBREF 0 30 30 58 58 0 1
#> urlThumbnailTeam 0 30 30 56 56 0 30
#>
#> Variable type: integer
#>
#> variable missing complete n mean sd p0 p25 p50 p75 p100 hist
#> ----------- --------- ---------- ---- --------- -------- --------- --------- --------- --------- --------- ----------
#> idTeamNBA 0 30 30 1.6e+09 497.55 1.6e+09 1.6e+09 1.6e+09 1.6e+09 1.6e+09 ▁▁▁▁▁▁▁▇
#>
#> Variable type: logical
#>
#> variable missing complete n mean count
#> --------------- --------- ---------- ---- ------ -------------------------
#> isPlayoffTeam 0 30 30 0.43 FAL: 17, TRU: 13, NA: 0
#>
#> Variable type: numeric
#>
#> variable missing complete n mean sd p0 p25 p50 p75 p100 hist
#> ------------------------ --------- ---------- ---- -------- ------- ------- -------- ------- -------- ------- ----------
#> astPerGameOpponent 0 30 30 24.56 1.29 21.3 23.55 24.55 25.6 27 ▁▁▅▅▇▂▆▃
#> astPerGameTeam 0 30 30 24.56 2.11 20 23.2 24.55 26 29.4 ▃▁▆▇▆▇▁▁
#> blkPerGameOpponent 0 30 30 4.97 0.61 3.7 4.6 5.05 5.38 6.1 ▃▁▇▃▇▇▅▂
#> blkPerGameTeam 0 30 30 4.97 0.73 2.4 4.7 5 5.4 6.4 ▁▁▁▃▆▇▃▁
#> countGamesTeam 0 30 30 79.6 0.5 79 79 80 80 80 ▅▁▁▁▁▁▁▇
#> drbPerGameOpponent 0 30 30 34.77 1.11 32.5 34.12 34.75 35.62 36.8 ▂▃▃▇▃▇▇▂
#> drbPerGameTeam 0 30 30 34.78 1.8 31.2 33.9 34.75 35.72 40.3 ▂▂▇▇▅▁▁▁
#> fg2aPerGameOpponent 0 30 30 57.21 2.48 50.9 55.85 57.25 58.5 62.3 ▁▁▂▃▇▃▂▂
#> fg2aPerGameTeam 0 30 30 57.23 4.55 42.1 55.18 57.35 61.08 63.3 ▁▁▁▂▅▇▆▇
#> fg2mPerGameOpponent 0 30 30 29.72 1.49 25.8 29.33 30 30.48 31.9 ▂▁▂▂▃▇▃▅
#> fg2mPerGameTeam 0 30 30 29.73 2.16 23.1 28.33 30.2 31.28 33.4 ▁▁▂▂▇▇▇▃
#> fg3aPerGameOpponent 0 30 30 31.93 1.99 27.8 30.2 32.3 33.5 36 ▂▃▃▃▇▇▂▁
#> fg3aPerGameTeam 0 30 30 31.93 4.26 25.3 29.15 31.75 34.3 45.1 ▅▇▇▇▃▁▁▁
#> fg3mPerGameOpponent 0 30 30 11.33 0.87 9.6 10.62 11.55 11.9 12.9 ▂▅▅▃▅▇▅▂
#> fg3mPerGameTeam 0 30 30 11.32 1.5 9.1 10.05 11.15 12.25 16 ▆▅▇▅▃▁▁▁
#> fgaPerGameOpponent 0 30 30 89.14 2.44 83 87.75 89.3 90.57 93.4 ▁▁▅▆▆▇▃▅
#> fgaPerGameTeam 0 30 30 89.14 2.11 84.3 87.73 89.1 90.47 93.9 ▁▂▇▇▆▇▁▂
#> fgmPerGameOpponent 0 30 30 41.04 1.5 37.4 40.15 41.25 42 43.4 ▁▂▂▇▃▅▇▅
#> fgmPerGameTeam 0 30 30 41.05 1.66 37.8 39.95 41.35 42.27 43.9 ▂▅▂▇▇▇▃▃
#> ftaPerGameOpponent 0 30 30 23.15 1.96 20.2 22.13 22.95 24.03 28.1 ▅▂▇▅▃▁▁▂
#> ftaPerGameTeam 0 30 30 23.13 2.22 19.2 21.2 23.15 24.4 28.5 ▂▅▂▇▃▃▁▂
#> ftmPerGameOpponent 0 30 30 17.74 1.51 15.3 16.77 17.8 18.3 21.3 ▃▃▃▇▂▁▁▂
#> ftmPerGameTeam 0 30 30 17.74 1.72 14.9 16.5 17.7 18.5 22.7 ▃▆▆▇▃▁▁▁
#> minutesPerGameOpponent 0 30 30 241.64 0.83 240.3 241 241.6 242.2 243.8 ▅▂▇▂▅▂▁▁
#> minutesPerGameTeam 0 30 30 241.64 0.83 240.3 241 241.6 242.2 243.8 ▅▂▇▂▅▂▁▁
#> orbPerGameOpponent 0 30 30 10.34 0.76 8.9 9.9 10.3 10.9 11.8 ▅▂▅▇▅▃▇▂
#> orbPerGameTeam 0 30 30 10.34 0.99 8.7 9.7 10.1 11.07 12.5 ▂▅▇▂▃▃▂▁
#> pctFG2PerGameOpponent 0 30 30 0.52 0.018 0.48 0.51 0.52 0.53 0.56 ▁▂▅▇▃▇▂▁
#> pctFG2PerGameTeam 0 30 30 0.52 0.02 0.48 0.51 0.52 0.53 0.56 ▂▂▇▅▅▃▂▂
#> pctFG3PerGameOpponent 0 30 30 0.35 0.011 0.34 0.34 0.35 0.36 0.38 ▇▅▂▃▇▂▁▂
#> pctFG3PerGameTeam 0 30 30 0.36 0.015 0.33 0.35 0.35 0.36 0.39 ▂▆▇▇▂▂▁▂
#> pctFGPerGameOpponent 0 30 30 0.46 0.013 0.43 0.45 0.46 0.47 0.49 ▁▂▇▆▇▂▂▁
#> pctFGPerGameTeam 0 30 30 0.46 0.013 0.43 0.45 0.46 0.47 0.49 ▁▃▇▂▅▇▂▁
#> pctFTPerGameOpponent 0 30 30 0.77 0.011 0.74 0.76 0.77 0.77 0.79 ▁▁▇▃▇▇▅▅
#> pctFTPerGameTeam 0 30 30 0.77 0.033 0.69 0.75 0.77 0.79 0.82 ▂▂▂▅▇▇▇▅
#> pfPerGameOpponent 0 30 30 20.98 1.27 18.8 20.1 20.8 21.88 24 ▂▇▇▆▃▆▁▂
#> pfPerGameTeam 0 30 30 20.97 1.32 18.2 20.15 21.05 21.65 23.7 ▂▂▃▆▇▃▁▃
#> ptsPerGameOpponent 0 30 30 111.15 3.87 104.3 107.97 111 113.88 119.2 ▅▅▃▇▅▇▃▁
#> ptsPerGameTeam 0 30 30 111.14 4.07 103 107.5 112.1 114.1 118.2 ▃▂▆▁▇▇▅▂
#> stlPerGameOpponent 0 30 30 7.66 0.71 6.5 7.17 7.55 7.8 9.9 ▂▂▇▃▂▁▁▁
#> stlPerGameTeam 0 30 30 7.66 0.85 6.1 6.9 7.6 8.38 9.5 ▂▇▁▇▁▇▂▁
#> tovPerGameOpponent 0 30 30 14.14 1.21 12.2 13.33 13.8 15.1 16.9 ▃▅▇▃▃▃▂▁
#> tovPerGameTeam 0 30 30 14.14 1.02 12 13.53 14 14.73 17.1 ▂▂▆▇▃▂▁▁
#> trbPerGameOpponent 0 30 30 45.1 1.47 42.2 44.02 45.25 45.88 47.9 ▃▂▅▇▇▇▂▃
#> trbPerGameTeam 0 30 30 45.12 2.1 40.4 44.15 45.3 46.27 49.6 ▂▂▃▅▇▆▃▁
#> yearSeason 0 30 30 2019 0 2019 2019 2019 2019 2019 ▁▁▁▇▁▁▁▁Team Per-Possession Summaries
Since there’s not much in the way of missing data, and I’d like to be able to see the histograms generated by skimr, I’m going to use skim_to_list() and be a bit more selective about which summary statistics I want to show.
skimmed_list_per_poss <- skim_to_list(dataBREFPerPossTeams)
numeric_skim_per_poss <- skimmed_list_per_poss$numeric
numeric_skim_per_poss %>%
select(one_of(c("variable", "mean", "sd", "p0", "p25", "p50", "p75", "p100", "hist"))) %>%
mutate(mean = as.numeric(mean),
sd = as.numeric(sd)) %>%
mutate_at(vars(starts_with("p")), as.numeric) %>%
select(-p0) %>%
skimr::kable() %>%
kableExtra::kable_styling(row_label_position = "l")| variable | mean | sd | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|
| astPerPossOpponent | 24.39 | 1.180 | 23.70 | 24.15 | 25.48 | 26.30 | ▁▁▃▅▇▃▅▅ |
| astPerPossTeam | 24.37 | 1.980 | 23.20 | 24.50 | 25.75 | 29.00 | ▁▃▇▆▇▇▁▂ |
| blkPerPossOpponent | 4.93 | 0.620 | 4.53 | 4.90 | 5.27 | 6.10 | ▃▁▃▃▇▅▃▃ |
| blkPerPossTeam | 4.93 | 0.700 | 4.53 | 5.00 | 5.27 | 6.30 | ▁▁▁▃▂▇▃▁ |
| countGamesTeam | 79.60 | 0.500 | 79.00 | 80.00 | 80.00 | 80.00 | ▅▁▁▁▁▁▁▇ |
| drbPerPossOpponent | 34.54 | 1.100 | 33.70 | 34.50 | 35.35 | 36.60 | ▃▅▇▇▃▅▂▃ |
| drbPerPossTeam | 34.53 | 1.590 | 33.70 | 34.65 | 35.45 | 38.90 | ▂▂▃▇▇▅▁▁ |
| fg2aPerPossOpponent | 56.80 | 2.110 | 55.42 | 56.85 | 57.77 | 60.80 | ▁▂▇▇▇▇▁▅ |
| fg2aPerPossTeam | 56.80 | 4.470 | 54.45 | 57.20 | 60.05 | 63.80 | ▁▁▁▃▃▇▇▃ |
| fg2mPerPossOpponent | 29.52 | 1.380 | 29.02 | 29.85 | 30.55 | 32.20 | ▃▁▃▇▆▇▅▁ |
| fg2mPerPossTeam | 29.51 | 1.970 | 28.50 | 29.70 | 30.78 | 32.70 | ▁▁▂▂▅▇▅▃ |
| fg3aPerPossOpponent | 31.69 | 1.860 | 30.10 | 32.55 | 33.00 | 34.70 | ▂▁▅▂▁▇▇▁ |
| fg3aPerPossTeam | 31.70 | 4.250 | 28.82 | 31.55 | 34.00 | 45.80 | ▃▇▅▇▂▁▁▁ |
| fg3mPerPossOpponent | 11.24 | 0.840 | 10.60 | 11.45 | 11.88 | 12.60 | ▃▃▇▁▇▅▇▃ |
| fg3mPerPossTeam | 11.23 | 1.490 | 9.93 | 11.00 | 12.28 | 16.20 | ▇▆▅▇▂▁▁▁ |
| fgaPerPossOpponent | 88.49 | 1.440 | 87.70 | 88.95 | 89.50 | 90.70 | ▂▁▂▆▃▅▇▃ |
| fgaPerPossTeam | 88.51 | 1.690 | 87.32 | 88.40 | 90.17 | 91.90 | ▅▁▇▆▃▇▃▁ |
| fgmPerPossOpponent | 40.75 | 1.280 | 39.75 | 40.65 | 41.68 | 44.40 | ▃▇▇▇▇▁▁▁ |
| fgmPerPossTeam | 40.74 | 1.300 | 39.73 | 40.70 | 41.85 | 43.30 | ▂▂▇▅▆▆▂▃ |
| ftaPerPossOpponent | 22.95 | 1.770 | 22.00 | 22.70 | 24.00 | 27.30 | ▃▃▇▅▆▁▁▂ |
| ftaPerPossTeam | 22.96 | 2.040 | 21.50 | 22.70 | 24.23 | 27.90 | ▃▆▇▇▃▅▁▂ |
| ftmPerPossOpponent | 17.60 | 1.370 | 17.00 | 17.40 | 18.28 | 20.50 | ▂▂▂▇▃▂▁▃ |
| ftmPerPossTeam | 17.59 | 1.620 | 16.20 | 17.40 | 18.48 | 22.10 | ▃▆▇▅▃▁▁▁ |
| minutesPerPossOpponent | 19234.00 | 144.440 | 19116.25 | 19275.00 | 19325.00 | 19500.00 | ▅▅▅▁▇▅▅▂ |
| minutesPerPossTeam | 19234.00 | 144.440 | 19116.25 | 19275.00 | 19325.00 | 19500.00 | ▅▅▅▁▇▅▅▂ |
| orbPerPossOpponent | 10.25 | 0.660 | 9.90 | 10.30 | 10.70 | 11.60 | ▁▃▃▇▇▆▅▁ |
| orbPerPossTeam | 10.26 | 0.960 | 9.53 | 10.15 | 10.93 | 12.20 | ▇▇▃▇▃▃▃▂ |
| pctFG2PerPossOpponent | 0.52 | 0.018 | 0.51 | 0.52 | 0.53 | 0.56 | ▁▂▅▇▃▇▂▁ |
| pctFG2PerPossTeam | 0.52 | 0.020 | 0.51 | 0.52 | 0.53 | 0.56 | ▂▂▇▅▅▃▂▂ |
| pctFG3PerPossOpponent | 0.35 | 0.011 | 0.34 | 0.35 | 0.36 | 0.38 | ▇▅▂▃▇▂▁▂ |
| pctFG3PerPossTeam | 0.36 | 0.015 | 0.35 | 0.35 | 0.36 | 0.39 | ▂▆▇▇▂▂▁▂ |
| pctFGPerPossOpponent | 0.46 | 0.013 | 0.45 | 0.46 | 0.47 | 0.49 | ▁▂▇▆▇▂▂▁ |
| pctFGPerPossTeam | 0.46 | 0.013 | 0.45 | 0.46 | 0.47 | 0.49 | ▁▃▇▂▅▇▂▁ |
| pctFTPerPossOpponent | 0.77 | 0.011 | 0.76 | 0.77 | 0.77 | 0.79 | ▁▁▇▃▇▇▅▅ |
| pctFTPerPossTeam | 0.77 | 0.033 | 0.75 | 0.77 | 0.79 | 0.82 | ▂▂▂▅▇▇▇▅ |
| pfPerPossOpponent | 20.82 | 1.150 | 20.22 | 20.70 | 21.78 | 23.50 | ▃▂▇▇▁▆▁▂ |
| pfPerPossTeam | 20.82 | 1.200 | 20.22 | 20.75 | 21.35 | 23.30 | ▃▁▃▇▇▁▃▂ |
| ptsPerPossOpponent | 110.34 | 2.940 | 108.12 | 110.20 | 112.55 | 117.40 | ▃▆▆▇▅▅▁▁ |
| ptsPerPossTeam | 110.33 | 3.010 | 108.20 | 110.35 | 112.62 | 115.80 | ▅▁▇▇▅▇▃▃ |
| stlPerPossOpponent | 7.59 | 0.620 | 7.25 | 7.50 | 7.90 | 9.40 | ▃▅▇▇▃▁▁▁ |
| stlPerPossTeam | 7.59 | 0.820 | 6.93 | 7.45 | 8.17 | 9.10 | ▂▇▇▅▂▇▃▃ |
| tovPerPossOpponent | 14.03 | 1.170 | 13.12 | 13.75 | 15.05 | 16.30 | ▃▇▅▁▃▃▃▂ |
| tovPerPossTeam | 14.02 | 0.880 | 13.53 | 14.05 | 14.38 | 16.30 | ▂▂▅▇▅▂▁▁ |
| trbPerPossOpponent | 44.78 | 1.220 | 44.02 | 44.80 | 45.85 | 47.20 | ▃▁▇▇▅▇▇▁ |
| trbPerPossTeam | 44.78 | 1.850 | 44.00 | 44.75 | 45.98 | 47.90 | ▁▁▂▁▇▅▃▃ |
| yearSeason | 2019.00 | 0.000 | 2019.00 | 2019.00 | 2019.00 | 2019.00 | ▁▁▁▇▁▁▁▁ |
Team “Miscellaneous” Stats
I’ll use the same approach as I did above for the “Miscellaneous” stats.
| variable | mean | sd | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|
| ageMeanMisc | 26.2800 | 1.380 | 25.45 | 26.30 | 27.00 | 29.20 | ▃▁▃▇▇▃▁▃ |
| attendanceArenaTeam | 709709.4000 | 66261.380 | 662416.50 | 725228.50 | 752094.75 | 818145.00 | ▅▃▂▅▇▇▂▅ |
| attendancePerGameTeam | 17827.4000 | 1662.730 | 16560.25 | 18130.50 | 19258.00 | 20454.00 | ▇▂▅▇▆▆▆▆ |
| drtgTeamMisc | 110.3400 | 2.940 | 108.12 | 110.20 | 112.55 | 117.40 | ▃▆▆▇▅▅▁▁ |
| lossesPythagTeam | 39.6700 | 11.330 | 29.50 | 40.50 | 43.75 | 61.00 | ▂▆▃▃▇▂▁▃ |
| lossesTeam | 39.8000 | 11.680 | 30.50 | 40.00 | 47.75 | 64.00 | ▅▅▇▇▃▅▁▃ |
| marginVictoryTeam | 0.0033 | 4.870 | -1.72 | -0.33 | 4.01 | 9.13 | ▃▁▂▇▂▅▃▁ |
| nrtgTeamMisc | -0.0033 | 4.820 | -1.73 | -0.35 | 4.05 | 8.80 | ▃▁▂▇▃▃▆▁ |
| ortgTeamMisc | 110.3300 | 3.010 | 108.20 | 110.35 | 112.62 | 115.80 | ▅▁▇▇▅▇▃▃ |
| paceTeamMisc | 100.0500 | 2.120 | 98.15 | 100.00 | 101.55 | 103.80 | ▂▇▅▂▇▃▁▇ |
| pct3PRateMisc | 0.3600 | 0.048 | 0.33 | 0.35 | 0.38 | 0.52 | ▃▇▃▆▂▁▁▁ |
| pctDRBOpponentMisc | 77.0700 | 1.730 | 76.25 | 77.00 | 78.10 | 80.40 | ▁▂▁▅▇▅▂▂ |
| pctEFGTeamMisc | 0.5200 | 0.015 | 0.51 | 0.53 | 0.53 | 0.56 | ▁▂▆▃▇▂▁▁ |
| pctEFGTeamOppMisc | 0.5200 | 0.014 | 0.51 | 0.52 | 0.53 | 0.56 | ▂▇▃▃▂▃▁▁ |
| pctFTRateMisc | 0.2600 | 0.026 | 0.24 | 0.26 | 0.27 | 0.33 | ▂▅▅▇▂▂▁▂ |
| pctORBTeamMisc | 22.9000 | 1.880 | 21.90 | 22.50 | 24.50 | 26.90 | ▂▅▇▇▂▅▃▂ |
| pctTOVOpponentMisc | 12.4500 | 1.000 | 11.70 | 12.25 | 13.33 | 14.50 | ▆▇▅▁▆▆▁▃ |
| pctTOVTeamMisc | 12.4600 | 0.790 | 12.00 | 12.45 | 12.90 | 14.40 | ▃▃▆▇▅▃▁▁ |
| pctTrueShootingeTeamMisc | 0.5600 | 0.015 | 0.55 | 0.56 | 0.57 | 0.60 | ▁▇▇▇▅▇▁▁ |
| ratingSimpleSystemMisc | 0.0040 | 4.680 | -1.59 | -0.57 | 3.88 | 8.31 | ▃▁▁▇▂▂▅▂ |
| ratingStrengthOfScheduleMisc | 0.0000 | 0.370 | -0.34 | 0.06 | 0.27 | 0.69 | ▁▃▃▂▃▇▂▂ |
| ratioFTtoFGAOpponent | 0.2000 | 0.017 | 0.19 | 0.20 | 0.21 | 0.24 | ▂▃▇▇▇▂▁▃ |
| ratioFTtoFGATeam | 0.2000 | 0.020 | 0.18 | 0.20 | 0.21 | 0.26 | ▅▇▇▇▅▁▁▁ |
| winsPythagTeam | 39.9300 | 11.280 | 36.00 | 39.00 | 49.75 | 60.00 | ▃▁▂▇▂▂▆▁ |
| winsTeam | 39.8000 | 11.660 | 32.00 | 39.50 | 48.75 | 59.00 | ▃▁▂▇▇▇▇▃ |
| yearSeason | 2019.0000 | 0.000 | 2019.00 | 2019.00 | 2019.00 | 2019.00 | ▁▁▁▇▁▁▁▁ |
You can also see all of the variables in the data frame, dataBREFTeamJoined, but, with 220+ variables and different units of observation (e.g. per game versus per possession), I prefer to look at things separately.
Since I like to include the date in things, I’ll create a yesterday object. I’m also sourcing a string of footnote glyphs I like to use, since they’re annoying to type.
Better tables with {gt}
Let’s make some pretty tables using Rich Iannone’s {gt}. For some reason, basketball-reference has the incorrect logo for Denver, so I’ll use case_when() to fix that particular instance of urlThumbnailTeam.
| NBA Team Ratings | ||||||||
|---|---|---|---|---|---|---|---|---|
| NBA 2018-2019 Regular Season through 2019-04-07 | ||||||||
| Team | Wins | Losses | Ratings | Pace † | ||||
| Offensive | Defensive | Net* | ||||||
 |
Milwaukee Bucks | 59 | 21 | 114.0 | 105.2 | 8.8 | 103.2 | |
 |
Toronto Raptors | 56 | 24 | 113.1 | 107.3 | 5.8 | 100.2 | |
 |
Golden State Warriors | 55 | 24 | 115.8 | 109.5 | 6.3 | 100.8 | |
 |
Denver Nuggets | 53 | 26 | 113.2 | 108.8 | 4.4 | 97.7 | |
 |
Houston Rockets | 52 | 28 | 115.2 | 110.7 | 4.5 | 97.8 | |
 |
Portland Trail Blazers | 50 | 29 | 114.5 | 110.3 | 4.2 | 99.1 | |
 |
Philadelphia 76ers | 50 | 30 | 112.7 | 109.9 | 2.8 | 101.7 | |
 |
Utah Jazz | 49 | 30 | 110.7 | 105.3 | 5.4 | 100.3 | |
 |
Boston Celtics | 48 | 32 | 112.3 | 107.7 | 4.6 | 99.6 | |
 |
Indiana Pacers | 47 | 33 | 109.8 | 106.2 | 3.6 | 98.1 | |
 |
Los Angeles Clippers | 47 | 33 | 112.4 | 111.3 | 1.1 | 101.6 | |
 |
Oklahoma City Thunder | 46 | 33 | 109.9 | 106.8 | 3.1 | 102.9 | |
 |
San Antonio Spurs | 46 | 34 | 112.9 | 111.6 | 1.3 | 98.3 | |
 |
Orlando Magic | 40 | 40 | 108.5 | 108.0 | 0.5 | 98.1 | |
 |
Brooklyn Nets | 40 | 40 | 109.6 | 110.1 | -0.5 | 100.8 | |
 |
Detroit Pistons | 39 | 40 | 108.9 | 109.4 | -0.5 | 97.6 | |
 |
Sacramento Kings | 39 | 41 | 110.0 | 111.0 | -1.0 | 103.2 | |
 |
Miami Heat | 38 | 41 | 107.5 | 107.7 | -0.2 | 98.1 | |
 |
Charlotte Hornets | 37 | 42 | 110.9 | 112.4 | -1.5 | 99.0 | |
 |
Minnesota Timberwolves | 36 | 43 | 111.6 | 112.8 | -1.2 | 100.2 | |
 |
Los Angeles Lakers | 36 | 44 | 107.7 | 109.5 | -1.8 | 103.3 | |
 |
New Orleans Pelicans | 32 | 48 | 111.3 | 112.6 | -1.3 | 103.3 | |
 |
Memphis Grizzlies | 32 | 47 | 105.7 | 108.5 | -2.8 | 96.6 | |
 |
Washington Wizards | 32 | 48 | 111.1 | 113.9 | -2.8 | 101.4 | |
 |
Dallas Mavericks | 31 | 48 | 109.4 | 110.7 | -1.3 | 98.9 | |
 |
Atlanta Hawks | 29 | 51 | 108.1 | 113.8 | -5.7 | 103.8 | |
 |
Chicago Bulls | 22 | 58 | 104.9 | 113.2 | -8.3 | 99.0 | |
 |
Phoenix Suns | 19 | 61 | 105.9 | 114.7 | -8.8 | 100.4 | |
 |
Cleveland Cavaliers | 19 | 61 | 107.9 | 117.4 | -9.5 | 96.7 | |
 |
New York Knicks | 15 | 64 | 104.5 | 113.8 | -9.3 | 99.8 | |
| source: basketball-reference.com via nbastatR | ||||||||
| * Net Rating: an estimate of point differential per 100 possessions. † Pace: an estimate of possessions per 48 minutes. |
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