The Lahman package contains a database of pitching, hitting and fielding statistics from Major League Baseball from 1871 to 2022 including data from the present leagues American and National, and the four other major leagues, (American Association, Union Association, Player League, Federal League) and the National Association of 1871 to 1875.
The data is comprised of the following main tables:
People - player names, date of birth, death and other biological information.
Batting - batting statistics
Pitching - pitching statistics
Fielding - fielding statistics
A collection of other tables is also provided:
Teams:
Teams |
yearly stats and standings |
TeamsHalf |
split season data for teams |
TeamsFranchises |
franchise information |
Post-season play:
BattingPost |
post-season batting statistics |
PitchingPost |
post-season pitching statistics |
FieldingPost |
post-season fielding data |
SeriesPost |
post-season series information |
Awards:
AwardsManagers |
awards won by managers |
AwardsPlayers |
awards won by players |
AwardsShareManagers |
award voting for manager awards |
AwardsSharePlayers |
award voting for player awards |
Hall of Fame: links to People via hofID
HallOfFame |
Hall of Fame voting data |
Information is different tables relating to a player is tagged with his playerID and are linked to names and birthdates in the People table.
Other tables:
AllstarFull - All-Star games appearances; Managers - managerial statistics; FieldingOF - outfield position data; ManagersHalf - split season data for managers; Salaries - player salary data; Appearances - data on player appearances; Schools - Information on schools players attended; CollegePlaying - Information on schools players attended, by player and year;
Variable label tables are provided for some of the tables:
battingLabels, pitchingLabels, fieldingLabels
Lahman, S. (2023) Lahman’s Baseball Database, 1871-2022, Main page, https://www.seanlahman.com/baseball-archive/statistics/
library(Lahman)
library(tidyr)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(ggplot2)
library(readr)
library(caret)Loading required package: latticeView the Lahman package to display the dataset with the data dictionary on the baseball data
teams <- Teams
head(teams) yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin LgWin
1 1871 NA BS1 BNA <NA> 3 31 NA 20 10 <NA> <NA> N
2 1871 NA CH1 CNA <NA> 2 28 NA 19 9 <NA> <NA> N
3 1871 NA CL1 CFC <NA> 8 29 NA 10 19 <NA> <NA> N
4 1871 NA FW1 KEK <NA> 7 19 NA 7 12 <NA> <NA> N
5 1871 NA NY2 NNA <NA> 5 33 NA 16 17 <NA> <NA> N
6 1871 NA PH1 PNA <NA> 1 28 NA 21 7 <NA> <NA> Y
WSWin R AB H X2B X3B HR BB SO SB CS HBP SF RA ER ERA CG SHO SV
1 <NA> 401 1372 426 70 37 3 60 19 73 16 NA NA 303 109 3.55 22 1 3
2 <NA> 302 1196 323 52 21 10 60 22 69 21 NA NA 241 77 2.76 25 0 1
3 <NA> 249 1186 328 35 40 7 26 25 18 8 NA NA 341 116 4.11 23 0 0
4 <NA> 137 746 178 19 8 2 33 9 16 4 NA NA 243 97 5.17 19 1 0
5 <NA> 302 1404 403 43 21 1 33 15 46 15 NA NA 313 121 3.72 32 1 0
6 <NA> 376 1281 410 66 27 9 46 23 56 12 NA NA 266 137 4.95 27 0 0
IPouts HA HRA BBA SOA E DP FP name
1 828 367 2 42 23 243 24 0.834 Boston Red Stockings
2 753 308 6 28 22 229 16 0.829 Chicago White Stockings
3 762 346 13 53 34 234 15 0.818 Cleveland Forest Citys
4 507 261 5 21 17 163 8 0.803 Fort Wayne Kekiongas
5 879 373 7 42 22 235 14 0.840 New York Mutuals
6 747 329 3 53 16 194 13 0.845 Philadelphia Athletics
park attendance BPF PPF teamIDBR teamIDlahman45
1 South End Grounds I NA 103 98 BOS BS1
2 Union Base-Ball Grounds NA 104 102 CHI CH1
3 National Association Grounds NA 96 100 CLE CL1
4 Hamilton Field NA 101 107 KEK FW1
5 Union Grounds (Brooklyn) NA 90 88 NYU NY2
6 Jefferson Street Grounds NA 102 98 ATH PH1
teamIDretro
1 BS1
2 CH1
3 CL1
4 FW1
5 NY2
6 PH1glimpse(teams)Rows: 3,015
Columns: 48
$ yearID <int> 1871, 1871, 1871, 1871, 1871, 1871, 1871, 1871, 1871, 1…
$ lgID <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ teamID <fct> BS1, CH1, CL1, FW1, NY2, PH1, RC1, TRO, WS3, BL1, BR1, …
$ franchID <fct> BNA, CNA, CFC, KEK, NNA, PNA, ROK, TRO, OLY, BLC, ECK, …
$ divID <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Rank <int> 3, 2, 8, 7, 5, 1, 9, 6, 4, 2, 9, 6, 1, 7, 8, 3, 4, 5, 1…
$ G <int> 31, 28, 29, 19, 33, 28, 25, 29, 32, 58, 29, 37, 48, 22,…
$ Ghome <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ W <int> 20, 19, 10, 7, 16, 21, 4, 13, 15, 35, 3, 9, 39, 6, 5, 3…
$ L <int> 10, 9, 19, 12, 17, 7, 21, 15, 15, 19, 26, 28, 8, 16, 19…
$ DivWin <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ WCWin <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ LgWin <chr> "N", "N", "N", "N", "N", "Y", "N", "N", "N", "N", "N", …
$ WSWin <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ R <int> 401, 302, 249, 137, 302, 376, 231, 351, 310, 617, 152, …
$ AB <int> 1372, 1196, 1186, 746, 1404, 1281, 1036, 1248, 1353, 25…
$ H <int> 426, 323, 328, 178, 403, 410, 274, 384, 375, 753, 248, …
$ X2B <int> 70, 52, 35, 19, 43, 66, 44, 51, 54, 106, 29, 35, 107, 2…
$ X3B <int> 37, 21, 40, 8, 21, 27, 25, 34, 26, 31, 9, 10, 30, 5, 9,…
$ HR <int> 3, 10, 7, 2, 1, 9, 3, 6, 6, 14, 0, 1, 7, 0, 2, 4, 4, 5,…
$ BB <int> 60, 60, 26, 33, 33, 46, 38, 49, 48, 29, 18, 19, 29, 17,…
$ SO <int> 19, 22, 25, 9, 15, 23, 30, 19, 13, 28, 40, 25, 26, 13, …
$ SB <int> 73, 69, 18, 16, 46, 56, 53, 62, 48, 53, 8, 19, 48, 12, …
$ CS <int> 16, 21, 8, 4, 15, 12, 10, 24, 13, 18, 13, 16, 14, 3, 7,…
$ HBP <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ SF <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ RA <int> 303, 241, 341, 243, 313, 266, 287, 362, 303, 434, 413, …
$ ER <int> 109, 77, 116, 97, 121, 137, 108, 153, 137, 166, 160, 16…
$ ERA <dbl> 3.55, 2.76, 4.11, 5.17, 3.72, 4.95, 4.30, 5.51, 4.37, 2…
$ CG <int> 22, 25, 23, 19, 32, 27, 23, 28, 32, 48, 28, 37, 41, 15,…
$ SHO <int> 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 4, 0, 0, 3, 1, 2, 0…
$ SV <int> 3, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 4, 0, 0, 1, 0, 1, 0…
$ IPouts <int> 828, 753, 762, 507, 879, 747, 678, 750, 846, 1548, 778,…
$ HA <int> 367, 308, 346, 261, 373, 329, 315, 431, 371, 573, 484, …
$ HRA <int> 2, 6, 13, 5, 7, 3, 3, 4, 4, 3, 7, 6, 0, 6, 6, 2, 3, 2, …
$ BBA <int> 42, 28, 53, 21, 42, 53, 34, 75, 45, 63, 36, 21, 27, 24,…
$ SOA <int> 23, 22, 34, 17, 22, 16, 16, 12, 13, 77, 13, 13, 29, 11,…
$ E <int> 243, 229, 234, 163, 235, 194, 220, 198, 218, 432, 274, …
$ DP <int> 24, 16, 15, 8, 14, 13, 14, 22, 20, 22, 9, 15, 44, 17, 1…
$ FP <dbl> 0.834, 0.829, 0.818, 0.803, 0.840, 0.845, 0.821, 0.845,…
$ name <chr> "Boston Red Stockings", "Chicago White Stockings", "Cle…
$ park <chr> "South End Grounds I", "Union Base-Ball Grounds", "Nati…
$ attendance <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ BPF <int> 103, 104, 96, 101, 90, 102, 97, 101, 94, 106, 87, 115, …
$ PPF <int> 98, 102, 100, 107, 88, 98, 99, 100, 98, 102, 96, 122, 1…
$ teamIDBR <chr> "BOS", "CHI", "CLE", "KEK", "NYU", "ATH", "ROK", "TRO",…
$ teamIDlahman45 <chr> "BS1", "CH1", "CL1", "FW1", "NY2", "PH1", "RC1", "TRO",…
$ teamIDretro <chr> "BS1", "CH1", "CL1", "FW1", "NY2", "PH1", "RC1", "TRO",…lets conduct exploratory data analysis
Winning a game in baseball is counted using run, so for our first exploration can we find the average number of runs made in every season in Major league baseball
teams_runs <- teams |>
mutate(runs_game = R/(W + L))
head(teams_runs) yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin LgWin
1 1871 NA BS1 BNA <NA> 3 31 NA 20 10 <NA> <NA> N
2 1871 NA CH1 CNA <NA> 2 28 NA 19 9 <NA> <NA> N
3 1871 NA CL1 CFC <NA> 8 29 NA 10 19 <NA> <NA> N
4 1871 NA FW1 KEK <NA> 7 19 NA 7 12 <NA> <NA> N
5 1871 NA NY2 NNA <NA> 5 33 NA 16 17 <NA> <NA> N
6 1871 NA PH1 PNA <NA> 1 28 NA 21 7 <NA> <NA> Y
WSWin R AB H X2B X3B HR BB SO SB CS HBP SF RA ER ERA CG SHO SV
1 <NA> 401 1372 426 70 37 3 60 19 73 16 NA NA 303 109 3.55 22 1 3
2 <NA> 302 1196 323 52 21 10 60 22 69 21 NA NA 241 77 2.76 25 0 1
3 <NA> 249 1186 328 35 40 7 26 25 18 8 NA NA 341 116 4.11 23 0 0
4 <NA> 137 746 178 19 8 2 33 9 16 4 NA NA 243 97 5.17 19 1 0
5 <NA> 302 1404 403 43 21 1 33 15 46 15 NA NA 313 121 3.72 32 1 0
6 <NA> 376 1281 410 66 27 9 46 23 56 12 NA NA 266 137 4.95 27 0 0
IPouts HA HRA BBA SOA E DP FP name
1 828 367 2 42 23 243 24 0.834 Boston Red Stockings
2 753 308 6 28 22 229 16 0.829 Chicago White Stockings
3 762 346 13 53 34 234 15 0.818 Cleveland Forest Citys
4 507 261 5 21 17 163 8 0.803 Fort Wayne Kekiongas
5 879 373 7 42 22 235 14 0.840 New York Mutuals
6 747 329 3 53 16 194 13 0.845 Philadelphia Athletics
park attendance BPF PPF teamIDBR teamIDlahman45
1 South End Grounds I NA 103 98 BOS BS1
2 Union Base-Ball Grounds NA 104 102 CHI CH1
3 National Association Grounds NA 96 100 CLE CL1
4 Hamilton Field NA 101 107 KEK FW1
5 Union Grounds (Brooklyn) NA 90 88 NYU NY2
6 Jefferson Street Grounds NA 102 98 ATH PH1
teamIDretro runs_game
1 BS1 13.366667
2 CH1 10.785714
3 CL1 8.586207
4 FW1 7.210526
5 NY2 9.151515
6 PH1 13.428571we can narrow down our analysis to find the average number of runs per games for every team for a given year for all teams
runs_per_yr <- teams_runs |>
group_by(yearID) |>
summarize(mean_runs = mean(runs_game, na.rm = TRUE))
head(runs_per_yr)# A tibble: 6 × 2
yearID mean_runs
<int> <dbl>
1 1871 10.5
2 1872 8.85
3 1873 8.21
4 1874 7.35
5 1875 5.54
6 1876 5.93# lets graph this summary and observe it over time
ggplot(runs_per_yr, aes(x = yearID, y = mean_runs)) +
geom_line() +
geom_point() +
labs(
title = "Average MLB Runs by Year",
caption = "Source: https://www.seanlahman.com/baseball-archive/statistics/"
) +
theme_minimal()
What team scored the most runs per year
runs_teams <- Teams |>
group_by(name) |>
filter(yearID == 2022) |>
select(name, R)
#head(runs_teams)
# arrange the Runs in descending order to view wha team made the highest runs
arrange(runs_teams, desc(R))# A tibble: 30 × 2
# Groups: name [30]
name R
<chr> <int>
1 Los Angeles Dodgers 847
2 New York Yankees 807
3 Atlanta Braves 789
4 Toronto Blue Jays 775
5 New York Mets 772
6 St. Louis Cardinals 772
7 Philadelphia Phillies 747
8 Houston Astros 737
9 Boston Red Sox 735
10 Milwaukee Brewers 725
# ℹ 20 more rows# plot
ggplot(runs_teams, aes(x = name, y = R)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(
title = "Runs scored by each team",
subtitle = "year 2022",
x = "Teams",
y = "Runs"
) +
theme_minimal()
What team scored the highest Homeruns in the year 2022
homeruns <- Teams |>
group_by(name) |>
filter(yearID == 2022) |>
select(name, H)
arrange(homeruns, desc(H))# A tibble: 30 × 2
# Groups: name [30]
name H
<chr> <int>
1 Toronto Blue Jays 1464
2 Chicago White Sox 1435
3 Boston Red Sox 1427
4 New York Mets 1422
5 Los Angeles Dodgers 1418
6 Cleveland Guardians 1410
7 Colorado Rockies 1408
8 Atlanta Braves 1394
9 Philadelphia Phillies 1392
10 St. Louis Cardinals 1386
# ℹ 20 more rows# plot
ggplot(homeruns, aes(x = name, y = H)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(
title = "Homeruns by each team",
subtitle = "year 2022",
x = "Teams",
y = "Homeruns"
) +
theme_minimal()
How does different metrics compare to various teams
# Restrict to AL and NL in mordern era
teams <- Teams |>
filter(yearID >= 2022 & lgID %in% c("AL", "NL")) |>
drop_na() |>
group_by(yearID, teamID) |>
mutate(TB = H + X2B + 2 * X3B + 3 * HR,
WinPct = W/G,
rpg = R/G,
hrpg = HR/G,
tbpg = TB/G,
kpg = SO/G,
k2bb = SO/BB,
whip = 3 * (H + BB) / IPouts)
# ggplot by year for selected team stats
yrPlot <- function(yvar, label)
{
ggplot(teams, aes_string(x = "yearID", y = yvar)) +
geom_point(size = 0.5) +
geom_smooth(method="loess") +
labs(x = "Year", y = paste(label, "per game"))
}Plot of win percentage against run differential (R - RA)
ggplot(teams, aes(x = R - RA, y = WinPct)) +
geom_point(size = 0.75) +
geom_smooth(method = "loess") +
geom_hline(yintercept = 0.5, color = "red") +
geom_vline(xintercept = 0, color = "orange") +
labs(
title = "Teams Win Percentage vs Run Differential",
x = "Run differential",
y = "Win percentage") +
theme_minimal()`geom_smooth()` using formula = 'y ~ x'
Teams with over 4 million attendance in a season
teams |>
filter(attendance >= 4e6) |>
select(yearID, lgID, teamID, Rank, attendance) |>
arrange(desc(attendance))# A tibble: 0 × 5
# Groups: yearID, teamID [0]
# ℹ 5 variables: yearID <int>, lgID <fct>, teamID <fct>, Rank <int>,
# attendance <int>head(teams)# A tibble: 6 × 56
# Groups: yearID, teamID [6]
yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin
<int> <fct> <fct> <fct> <chr> <int> <int> <int> <int> <int> <chr> <chr>
1 2022 NL ARI ARI W 4 162 81 74 88 N N
2 2022 NL ATL ATL E 1 162 81 101 61 Y N
3 2022 AL BAL BAL E 4 162 81 83 79 N N
4 2022 AL BOS BOS E 5 162 81 78 84 N N
5 2022 AL CHA CHW C 2 162 81 81 81 N N
6 2022 NL CHN CHC C 3 162 81 74 88 N N
# ℹ 44 more variables: LgWin <chr>, WSWin <chr>, R <int>, AB <int>, H <int>,
# X2B <int>, X3B <int>, HR <int>, BB <int>, SO <int>, SB <int>, CS <int>,
# HBP <int>, SF <int>, RA <int>, ER <int>, ERA <dbl>, CG <int>, SHO <int>,
# SV <int>, IPouts <int>, HA <int>, HRA <int>, BBA <int>, SOA <int>, E <int>,
# DP <int>, FP <dbl>, name <chr>, park <chr>, attendance <int>, BPF <int>,
# PPF <int>, teamIDBR <chr>, teamIDlahman45 <chr>, teamIDretro <chr>,
# TB <dbl>, WinPct <dbl>, rpg <dbl>, hrpg <dbl>, tbpg <dbl>, kpg <dbl>, …ggplot(teams, aes(x = yearID, y = attendance/1000)) +
geom_point() +
facet_wrap(~ lgID)
Average season Homeruns by Park, post-2000
teams %>%
filter(yearID >= 2000) %>%
group_by(park) %>%
summarise(meanHRpg = mean((HR + HRA)/Ghome), nyears = n()) %>%
filter(nyears >= 20) %>%
arrange(desc(meanHRpg)) %>%
head(., 10) # A tibble: 0 × 3
# ℹ 3 variables: park <chr>, meanHRpg <dbl>, nyears <int>head(teams)# A tibble: 6 × 56
# Groups: yearID, teamID [6]
yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin
<int> <fct> <fct> <fct> <chr> <int> <int> <int> <int> <int> <chr> <chr>
1 2022 NL ARI ARI W 4 162 81 74 88 N N
2 2022 NL ATL ATL E 1 162 81 101 61 Y N
3 2022 AL BAL BAL E 4 162 81 83 79 N N
4 2022 AL BOS BOS E 5 162 81 78 84 N N
5 2022 AL CHA CHW C 2 162 81 81 81 N N
6 2022 NL CHN CHC C 3 162 81 74 88 N N
# ℹ 44 more variables: LgWin <chr>, WSWin <chr>, R <int>, AB <int>, H <int>,
# X2B <int>, X3B <int>, HR <int>, BB <int>, SO <int>, SB <int>, CS <int>,
# HBP <int>, SF <int>, RA <int>, ER <int>, ERA <dbl>, CG <int>, SHO <int>,
# SV <int>, IPouts <int>, HA <int>, HRA <int>, BBA <int>, SOA <int>, E <int>,
# DP <int>, FP <dbl>, name <chr>, park <chr>, attendance <int>, BPF <int>,
# PPF <int>, teamIDBR <chr>, teamIDlahman45 <chr>, teamIDretro <chr>,
# TB <dbl>, WinPct <dbl>, rpg <dbl>, hrpg <dbl>, tbpg <dbl>, kpg <dbl>, …Ofcos every baseball fan wants his/her team to win. Lets go ahead and create a model to predict wins by team
base_df <- teams |>
drop_na() |>
select(name, yearID, W, L, R, H, X2B, X3B, HR, SO, RA) |>
filter(yearID >= 2009)Adding missing grouping variables: `teamID`head(base_df) # A tibble: 6 × 12
# Groups: yearID, teamID [6]
teamID name yearID W L R H X2B X3B HR SO RA
<fct> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 ARI Arizona D… 2022 74 88 702 1232 262 24 173 1341 740
2 ATL Atlanta B… 2022 101 61 789 1394 298 11 243 1498 609
3 BAL Baltimore… 2022 83 79 674 1281 275 25 171 1390 688
4 BOS Boston Re… 2022 78 84 735 1427 352 12 155 1373 787
5 CHA Chicago W… 2022 81 81 686 1435 272 9 149 1269 717
6 CHN Chicago C… 2022 74 88 657 1293 265 31 159 1448 731lets train a linear model based on the variables we filtered above and find out how statistically significant they are.
lm1 <- lm(W ~ R + H + X2B + X3B + HR + SO + RA, data = base_df)
summary(lm1)
Call:
lm(formula = W ~ R + H + X2B + X3B + HR + SO + RA, data = base_df)
Residuals:
Min 1Q Median 3Q Max
-6.4969 -1.6329 0.0259 2.5762 4.9378
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 89.821959 21.045980 4.268 0.000314 ***
R 0.080205 0.025537 3.141 0.004749 **
H 0.002605 0.014597 0.178 0.860013
X2B 0.045802 0.032690 1.401 0.175134
X3B 0.059980 0.088585 0.677 0.505411
HR 0.007321 0.044776 0.164 0.871610
SO -0.009420 0.008719 -1.080 0.291677
RA -0.100571 0.009168 -10.970 2.18e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.275 on 22 degrees of freedom
Multiple R-squared: 0.9622, Adjusted R-squared: 0.9502
F-statistic: 80.11 on 7 and 22 DF, p-value: 3.537e-14#observation
# we can observe that tripples(X3B), double(x2b) and strike outs(SO) are not statically significant we can create another model with only the statistically significant variable and compare
lm2 <- lm(W ~ R + H + RA, data = base_df)
summary(lm2)
Call:
lm(formula = W ~ R + H + RA, data = base_df)
Residuals:
Min 1Q Median 3Q Max
-8.6557 -1.5666 0.0654 2.6116 4.7752
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 69.168997 12.866651 5.376 1.25e-05 ***
R 0.078940 0.014397 5.483 9.43e-06 ***
H 0.021647 0.010548 2.052 0.0503 .
RA -0.103148 0.008748 -11.791 6.18e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.275 on 26 degrees of freedom
Multiple R-squared: 0.9554, Adjusted R-squared: 0.9502
F-statistic: 185.6 on 3 and 26 DF, p-value: < 2.2e-16using our second model we can try predicting team wins
preds <- predict(lm2, base_df)
#present the predicted value in a column to compare with the actual win value
base_df$pred <- preds
base_df# A tibble: 30 × 13
# Groups: yearID, teamID [30]
teamID name yearID W L R H X2B X3B HR SO RA
<fct> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 ARI Arizona … 2022 74 88 702 1232 262 24 173 1341 740
2 ATL Atlanta … 2022 101 61 789 1394 298 11 243 1498 609
3 BAL Baltimor… 2022 83 79 674 1281 275 25 171 1390 688
4 BOS Boston R… 2022 78 84 735 1427 352 12 155 1373 787
5 CHA Chicago … 2022 81 81 686 1435 272 9 149 1269 717
6 CHN Chicago … 2022 74 88 657 1293 265 31 159 1448 731
7 CIN Cincinna… 2022 62 100 648 1264 235 18 156 1430 815
8 CLE Clevelan… 2022 92 70 698 1410 273 31 127 1122 634
9 COL Colorado… 2022 68 94 698 1408 280 34 149 1330 873
10 DET Detroit … 2022 66 96 557 1240 235 27 110 1413 713
# ℹ 20 more rows
# ℹ 1 more variable: pred <dbl># plot the results
base_df |>
ggplot(aes(x = pred, y = W)) +
geom_point() +
geom_smooth() +
labs(
title = "Predicted wins against actual wins",
x = "Predicted Wins",
y = "Actual Wins"
) +
theme_minimal()`geom_smooth()` using method = 'loess' and formula = 'y ~ x'