Data Dive Contents:
- Finding out the summary of the numeric and categorical columns.
- Some analysis on batting averages on team wise, league wise and
their tendency over the period of time.
- Some analysis on Runs per game scored by teams wise, year wise.
- Finding the correlation between wins and runs scored, wins and suns
allowed.
- Interpreting the Home Runs hit by teams in three different division
(East, West, Central).
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(rmarkdown)
lahman_data = read.csv("/Users/anuragreddy/Desktop/Statistics with R/Lahmans Databse .csv")
head(lahman_data)
## yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin LgWin
## 1 2000 AL ANA ANA W 3 162 81 82 80 N N N
## 2 2000 AL BAL BAL E 4 162 81 74 88 N N N
## 3 2000 AL BOS BOS E 2 162 81 85 77 N N N
## 4 2000 AL CHA CHW C 1 162 81 95 67 Y N N
## 5 2000 AL CLE CLE C 2 162 81 90 72 N N N
## 6 2000 AL DET DET C 3 162 81 79 83 N N N
## WSWin R AB H X2B X3B HR BB SO SB CS HBP SF RA ER ERA CG SHO SV
## 1 N 864 5628 1574 309 34 236 608 1024 93 52 47 43 869 805 5.00 5 3 46
## 2 N 794 5549 1508 310 22 184 558 900 126 65 49 54 913 855 5.37 14 6 33
## 3 N 792 5630 1503 316 32 167 611 1019 43 30 42 48 745 683 4.23 7 12 46
## 4 N 978 5646 1615 325 33 216 591 960 119 42 53 61 839 751 4.66 5 7 43
## 5 N 950 5683 1639 310 30 221 685 1057 113 34 51 52 816 775 4.84 6 5 34
## 6 N 823 5644 1553 307 41 177 562 982 83 38 43 49 827 755 4.71 6 6 44
## IPouts HA HRA BBA SOA E DP FP name
## 1 4344 1534 228 662 846 134 182 0.978 Anaheim Angels
## 2 4300 1547 202 665 1017 116 151 0.981 Baltimore Orioles
## 3 4358 1433 173 498 1121 109 120 0.982 Boston Red Sox
## 4 4351 1509 195 614 1037 133 190 0.978 Chicago White Sox
## 5 4327 1511 173 666 1213 72 147 0.988 Cleveland Indians
## 6 4330 1583 177 496 978 105 171 0.983 Detroit Tigers
## park attendance BPF PPF teamIDBR teamIDlahman45
## 1 Edison International Field 2066982 102 103 ANA ANA
## 2 Oriole Park at Camden Yards 3297031 95 96 BAL BAL
## 3 Fenway Park II 2585895 104 103 BOS BOS
## 4 Comiskey Park II 1947799 102 102 CHW CHA
## 5 Jacobs Field 3456278 101 100 CLE CLE
## 6 Comerica Park 2438617 95 95 DET DET
## teamIDretro
## 1 ANA
## 2 BAL
## 3 BOS
## 4 CHA
## 5 CLE
## 6 DET
Question 1: Summary of Numeric and Categorical
columns.
#Summary of At-Bats in lahmandatabase
summary(lahman_data$AB)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1752 5462 5527 5368 5582 5770
#Summary of Home Runs in lahmandatabase
summary(lahman_data$HR)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 51.0 144.0 168.0 169.6 198.0 307.0
#Summary of divisions in lahmandatabase from 2000-2024
table(lahman_data$divID)
##
## C E W
## 233 220 207
summary(lahman_data$divID)
## Length Class Mode
## 660 character character
#Summary of leagues in lahmandatabase from 2000-2024
table(lahman_data$lgID)
##
## AL NL
## 317 343
#All numeric columns summary
lahman_data |>
summary(across(where(is.numeric)))
## yearID lgID teamID franchID
## Min. :2000 Length:660 Length:660 Length:660
## 1st Qu.:2005 Class :character Class :character Class :character
## Median :2010 Mode :character Mode :character Mode :character
## Mean :2011
## 3rd Qu.:2016
## Max. :2022
## divID Rank G Ghome
## Length:660 Min. :1.000 Min. : 58.0 Min. :24.00
## Class :character 1st Qu.:2.000 1st Qu.:162.0 1st Qu.:81.00
## Mode :character Median :3.000 Median :162.0 Median :81.00
## Mean :3.008 Mean :157.3 Mean :78.64
## 3rd Qu.:4.000 3rd Qu.:162.0 3rd Qu.:81.00
## Max. :6.000 Max. :163.0 Max. :84.00
## W L DivWin WCWin
## Min. : 19.00 Min. : 17.00 Length:660 Length:660
## 1st Qu.: 71.00 1st Qu.: 70.00 Class :character Class :character
## Median : 80.00 Median : 79.00 Mode :character Mode :character
## Mean : 78.65 Mean : 78.65
## 3rd Qu.: 90.00 3rd Qu.: 89.00
## Max. :116.00 Max. :119.00
## LgWin WSWin R AB
## Length:660 Length:660 Min. :219.0 Min. :1752
## Class :character Class :character 1st Qu.:671.0 1st Qu.:5462
## Mode :character Mode :character Median :730.0 Median :5527
## Mean :718.1 Mean :5368
## 3rd Qu.:790.0 3rd Qu.:5582
## Max. :978.0 Max. :5770
## H X2B X3B HR BB
## Min. : 390 Min. : 73.0 Min. : 3.00 Min. : 51.0 Min. :147.0
## 1st Qu.:1356 1st Qu.:264.8 1st Qu.:21.00 1st Qu.:144.0 1st Qu.:464.0
## Median :1426 Median :282.5 Median :28.00 Median :168.0 Median :514.0
## Mean :1387 Mean :276.8 Mean :28.04 Mean :169.6 Mean :506.5
## 3rd Qu.:1492 3rd Qu.:302.0 3rd Qu.:35.00 3rd Qu.:198.0 3rd Qu.:564.0
## Max. :1667 Max. :376.0 Max. :61.00 Max. :307.0 Max. :775.0
## SO SB CS HBP
## Min. : 440 Min. : 14.00 Min. : 3.00 Min. : 10.00
## 1st Qu.:1032 1st Qu.: 65.75 1st Qu.:27.00 1st Qu.: 47.00
## Median :1146 Median : 87.00 Median :34.00 Median : 55.00
## Mean :1145 Mean : 88.35 Mean :34.56 Mean : 56.52
## 3rd Qu.:1287 3rd Qu.:109.00 3rd Qu.:42.00 3rd Qu.: 66.00
## Max. :1596 Max. :200.00 Max. :74.00 Max. :112.00
## SF RA ER ERA
## Min. : 7.00 Min. :209.0 Min. :181.0 Min. :2.800
## 1st Qu.:36.00 1st Qu.:666.0 1st Qu.:609.0 1st Qu.:3.860
## Median :42.00 Median :729.0 Median :671.5 Median :4.210
## Mean :42.04 Mean :718.1 Mean :660.5 Mean :4.249
## 3rd Qu.:49.00 3rd Qu.:797.0 3rd Qu.:733.0 3rd Qu.:4.633
## Max. :75.00 Max. :981.0 Max. :913.0 Max. :5.840
## CG SHO SV IPouts
## Min. : 0.000 Min. : 0.000 Min. : 6.00 Min. :1419
## 1st Qu.: 2.000 1st Qu.: 6.000 1st Qu.:35.00 1st Qu.:4302
## Median : 4.000 Median : 9.000 Median :40.00 Median :4329
## Mean : 4.323 Mean : 9.174 Mean :39.53 Mean :4205
## 3rd Qu.: 6.000 3rd Qu.:12.000 3rd Qu.:45.00 3rd Qu.:4357
## Max. :18.000 Max. :23.000 Max. :66.00 Max. :4485
## HA HRA BBA SOA E
## Min. : 376 Min. : 62.0 Min. :145.0 Min. : 393 Min. : 20.00
## 1st Qu.:1356 1st Qu.:151.0 1st Qu.:466.8 1st Qu.:1034 1st Qu.: 86.00
## Median :1424 Median :170.0 Median :516.0 Median :1153 Median : 98.00
## Mean :1387 Mean :169.6 Mean :506.5 Mean :1145 Mean : 96.47
## 3rd Qu.:1493 3rd Qu.:190.0 3rd Qu.:560.0 3rd Qu.:1270 3rd Qu.:109.00
## Max. :1683 Max. :305.0 Max. :728.0 Max. :1671 Max. :145.00
## DP FP name park
## Min. : 33.0 Min. :0.9760 Length:660 Length:660
## 1st Qu.:132.0 1st Qu.:0.9820 Class :character Class :character
## Median :145.0 Median :0.9840 Mode :character Mode :character
## Mean :141.9 Mean :0.9835
## 3rd Qu.:158.0 3rd Qu.:0.9850
## Max. :204.0 Max. :0.9910
## attendance BPF PPF teamIDBR
## Min. : 0 Min. : 88.0 Min. : 88.0 Length:660
## 1st Qu.:1767692 1st Qu.: 97.0 1st Qu.: 97.0 Class :character
## Median :2321000 Median :100.0 Median :100.0 Mode :character
## Mean :2275828 Mean :100.1 Mean :100.1
## 3rd Qu.:2915270 3rd Qu.:103.0 3rd Qu.:103.0
## Max. :4298655 Max. :125.0 Max. :125.0
## teamIDlahman45 teamIDretro
## Length:660 Length:660
## Class :character Class :character
## Mode :character Mode :character
##
##
##
#Let's find the batting average of teams by grouping yearwise and league wise.
lahman_data$lgID
## [1] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [16] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [31] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [46] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [61] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [76] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [91] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [106] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [121] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [136] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [151] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [166] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [181] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [196] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [211] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [226] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [241] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [256] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [271] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [286] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [301] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [316] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [331] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [346] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [361] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "NL"
## [376] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [391] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL"
## [406] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [421] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL"
## [436] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [451] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL"
## [466] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [481] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL"
## [496] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [511] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL"
## [526] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [541] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL"
## [556] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [571] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL"
## [586] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [601] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL"
## [616] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
## [631] "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL" "AL"
## [646] "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL" "NL"
Question 2: Lets find out the Batting Average of teams group
by year and league wise.
p <- lahman_data |>
group_by(teamID,yearID,lgID) |>
summarise(Batting_Avg = sum(H)/sum(AB), .groups = "keep") |>
arrange(desc(Batting_Avg))
p
## # A tibble: 660 × 4
## # Groups: teamID, yearID, lgID [660]
## teamID yearID lgID Batting_Avg
## <chr> <int> <chr> <dbl>
## 1 COL 2000 NL 0.294
## 2 COL 2001 NL 0.292
## 3 NYA 2007 AL 0.290
## 4 BOS 2003 AL 0.289
## 5 CLE 2000 AL 0.288
## 6 SEA 2001 AL 0.288
## 7 KCA 2000 AL 0.288
## 8 MIN 2006 AL 0.287
## 9 DET 2007 AL 0.287
## 10 SEA 2007 AL 0.287
## # ℹ 650 more rows
Insights: Colorado is the only team from national league
has topped in the batting average in consecutive years 2000 and
2001.
Question 3: Lets find out which league has best batting
average and whats the tendency over the years.
# Lets visualize batting average of AL vs NL
k <- lahman_data|>
group_by(yearID,lgID)|>
summarize(Batting_Avg = sum(H)/sum(AB),.groups = 'keep')
k
## # A tibble: 44 × 3
## # Groups: yearID, lgID [44]
## yearID lgID Batting_Avg
## <int> <chr> <dbl>
## 1 2000 AL 0.276
## 2 2000 NL 0.266
## 3 2001 AL 0.267
## 4 2001 NL 0.261
## 5 2002 AL 0.264
## 6 2002 NL 0.259
## 7 2003 AL 0.267
## 8 2003 NL 0.262
## 9 2004 AL 0.270
## 10 2004 NL 0.263
## # ℹ 34 more rows
k |>
ggplot(aes(x=yearID,y=Batting_Avg,group=lgID,colour=lgID))+
geom_point()+
stat_smooth(se=FALSE)+
theme_classic()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Insights: We can interpret from the above visualization
that, Overall, the batting average of the teams has significantly
decreased over a period of time. In terms of batting average American
league has better batters than national league
players.
Question 4: Lets find out which teams has the highest Runs
per Game metric and in which year (2000-2020)
# Lets find out which team has the highest Runs per Game and in whcih year(2000-2020)
lahman_data |>
mutate(RPG = R/G) |>
arrange(desc(RPG))|>
select(yearID,teamID,RPG)
## yearID teamID RPG
## 1 2000 CHA 6.037037
## 2 2000 COL 5.975309
## 3 2007 NYA 5.975309
## 4 2003 BOS 5.932099
## 5 2000 OAK 5.881988
## 6 2000 CLE 5.864198
## 7 2004 BOS 5.858025
## 8 2019 NYA 5.820988
## 9 2020 LAN 5.816667
## 10 2020 ATL 5.800000
## 11 2019 MIN 5.796296
## 12 2000 HOU 5.790123
## 13 2006 NYA 5.740741
## 14 2001 SEA 5.722222
## 15 2000 SFN 5.709877
## 16 2001 COL 5.697531
## 17 2019 HOU 5.679012
## 18 2009 NYA 5.648148
## 19 2005 BOS 5.617284
## 20 2000 SEA 5.598765
## 21 2003 ATL 5.598765
## 22 2002 NYA 5.571429
## 23 2008 TEX 5.561728
## 24 2019 BOS 5.561728
## 25 2001 CLE 5.537037
## 26 2004 NYA 5.537037
## 27 2017 HOU 5.530864
## 28 2003 TOR 5.518519
## 29 2007 PHI 5.506173
## 30 2015 TOR 5.500000
## 31 2001 TEX 5.493827
## 32 2000 SLN 5.475309
## 33 2007 DET 5.475309
## 34 2005 NYA 5.469136
## 35 2019 LAN 5.469136
## 36 2001 OAK 5.456790
## 37 2009 LAA 5.450617
## 38 2000 KCA 5.425926
## 39 2016 BOS 5.419753
## 40 2020 SDN 5.416667
## 41 2000 NYA 5.409938
## 42 2003 SLN 5.407407
## 43 2011 BOS 5.401235
## 44 2019 WAS 5.388889
## 45 2009 BOS 5.382716
## 46 2003 NYA 5.380368
## 47 2006 CLE 5.370370
## 48 2006 CHA 5.358025
## 49 2007 BOS 5.351852
## 50 2011 NYA 5.351852
## 51 2004 CHA 5.339506
## 52 2005 TEX 5.339506
## 53 2006 PHI 5.339506
## 54 2000 ANA 5.333333
## 55 2021 HOU 5.327160
## 56 2000 TOR 5.314815
## 57 2008 CHN 5.310559
## 58 2004 TEX 5.308642
## 59 2002 BOS 5.302469
## 60 2010 NYA 5.302469
## 61 2004 CLE 5.296296
## 62 2017 NYA 5.296296
## 63 2021 TBA 5.290123
## 64 2002 CHA 5.283951
## 65 2004 SLN 5.277778
## 66 2011 TEX 5.277778
## 67 2019 ATL 5.277778
## 68 2007 COL 5.276074
## 69 2003 COL 5.265432
## 70 2013 BOS 5.265432
## 71 2002 ANA 5.253086
## 72 2020 NYA 5.250000
## 73 2004 SFN 5.246914
## 74 2006 ATL 5.240741
## 75 2000 TEX 5.234568
## 76 2001 HOU 5.228395
## 77 2022 LAN 5.228395
## 78 2021 TOR 5.222222
## 79 2008 BOS 5.216049
## 80 2016 COL 5.216049
## 81 2019 OAK 5.216049
## 82 2002 TEX 5.203704
## 83 2004 BAL 5.197531
## 84 2004 PHI 5.185185
## 85 2003 KCA 5.160494
## 86 2004 ANA 5.160494
## 87 2006 TEX 5.154321
## 88 2019 COL 5.154321
## 89 2006 NYN 5.148148
## 90 2004 COL 5.141975
## 91 2021 LAN 5.123457
## 92 2021 BOS 5.117284
## 93 2004 DET 5.104938
## 94 2020 CHA 5.100000
## 95 2020 PHI 5.100000
## 96 2003 TEX 5.098765
## 97 2017 COL 5.086420
## 98 2008 MIN 5.085890
## 99 2000 DET 5.080247
## 100 2006 DET 5.074074
## 101 2007 LAA 5.074074
## 102 2017 CHN 5.074074
## 103 2008 DET 5.067901
## 104 2006 BOS 5.061728
## 105 2006 LAN 5.061728
## 106 2009 PHI 5.061728
## 107 2000 CIN 5.061350
## 108 2002 ARI 5.055556
## 109 2017 WAS 5.055556
## 110 2001 ARI 5.049383
## 111 2010 BOS 5.049383
## 112 2017 CLE 5.049383
## 113 2007 TEX 5.037037
## 114 2020 TOR 5.033333
## 115 2017 MIN 5.030864
## 116 2005 CIN 5.030675
## 117 2001 SLN 5.024691
## 118 2002 SEA 5.024691
## 119 2019 CHN 5.024691
## 120 2002 TOR 5.018519
## 121 2006 COL 5.018519
## 122 2019 ARI 5.018519
## 123 2017 ARI 5.012346
## 124 2009 MIN 5.012270
## 125 2007 CLE 5.006173
## 126 2000 ATL 5.000000
## 127 2007 ATL 5.000000
## 128 2019 TEX 5.000000
## 129 2006 TOR 4.993827
## 130 2001 NYA 4.993789
## 131 2012 TEX 4.987654
## 132 2016 CHN 4.987654
## 133 2020 SFN 4.983333
## 134 2000 NYN 4.981481
## 135 2005 PHI 4.981481
## 136 2022 NYA 4.981481
## 137 2008 CHA 4.975460
## 138 2003 HOU 4.969136
## 139 2005 SLN 4.969136
## 140 2008 CLE 4.969136
## 141 2007 NYN 4.962963
## 142 2009 COL 4.962963
## 143 2012 NYA 4.962963
## 144 2021 SFN 4.962963
## 145 2004 ATL 4.956790
## 146 2004 HOU 4.956790
## 147 2009 TBA 4.956790
## 148 2010 TBA 4.950617
## 149 2003 MIN 4.944444
## 150 2006 MIN 4.944444
## 151 2007 MIL 4.944444
## 152 2002 OAK 4.938272
## 153 2001 SFN 4.932099
## 154 2008 NYN 4.932099
## 155 2008 PHI 4.932099
## 156 2017 TEX 4.932099
## 157 2000 LAN 4.925926
## 158 2001 CHA 4.925926
## 159 2009 TOR 4.925926
## 160 2013 DET 4.913580
## 161 2021 CHA 4.913580
## 162 2003 SEA 4.907407
## 163 2021 ATL 4.906832
## 164 2000 BAL 4.901235
## 165 2007 SEA 4.901235
## 166 2020 LAA 4.900000
## 167 2000 PIT 4.895062
## 168 2004 OAK 4.895062
## 169 2000 BOS 4.888889
## 170 2000 ARI 4.888889
## 171 2020 WAS 4.883333
## 172 2003 CHA 4.882716
## 173 2003 PHI 4.882716
## 174 2019 NYN 4.882716
## 175 2005 CLE 4.876543
## 176 2007 FLO 4.876543
## 177 2010 CIN 4.876543
## 178 2001 SDN 4.870370
## 179 2004 CHN 4.870370
## 180 2008 NYA 4.870370
## 181 2022 ATL 4.870370
## 182 2020 BOS 4.866667
## 183 2002 SLN 4.858025
## 184 2010 TEX 4.858025
## 185 2011 DET 4.858025
## 186 2008 BAL 4.857143
## 187 2021 CIN 4.851852
## 188 2006 SLN 4.850932
## 189 2009 MIL 4.845679
## 190 2017 BOS 4.845679
## 191 2009 TEX 4.839506
## 192 2002 SFN 4.833333
## 193 2007 CIN 4.833333
## 194 2013 SLN 4.833333
## 195 2007 TBA 4.827160
## 196 2016 CLE 4.826087
## 197 2010 MIN 4.820988
## 198 2020 TBA 4.816667
## 199 2004 MIN 4.814815
## 200 2009 LAN 4.814815
## 201 2008 SLN 4.808642
## 202 2016 SLN 4.808642
## 203 2002 COL 4.802469
## 204 2017 MIA 4.802469
## 205 2001 CHN 4.796296
## 206 2001 BOS 4.795031
## 207 2012 MIL 4.790123
## 208 2005 TOR 4.783951
## 209 2022 TOR 4.783951
## 210 2008 FLO 4.782609
## 211 2008 TBA 4.777778
## 212 2019 PHI 4.777778
## 213 2006 ARI 4.771605
## 214 2009 CLE 4.771605
## 215 2014 LAA 4.771605
## 216 2002 MIN 4.770186
## 217 2020 NYN 4.766667
## 218 2005 OAK 4.765432
## 219 2009 FLO 4.765432
## 220 2010 PHI 4.765432
## 221 2022 NYN 4.765432
## 222 2022 SLN 4.765432
## 223 2001 MIN 4.759259
## 224 2006 OAK 4.759259
## 225 2010 COL 4.753086
## 226 2017 LAN 4.753086
## 227 2005 ATL 4.746914
## 228 2019 CLE 4.746914
## 229 2019 LAA 4.746914
## 230 2019 TBA 4.746914
## 231 2019 MIL 4.746914
## 232 2003 OAK 4.740741
## 233 2004 SDN 4.740741
## 234 2006 BAL 4.740741
## 235 2016 SEA 4.740741
## 236 2001 TOR 4.734568
## 237 2012 LAA 4.734568
## 238 2013 OAK 4.734568
## 239 2006 LAA 4.728395
## 240 2008 LAA 4.722222
## 241 2012 SLN 4.722222
## 242 2016 TEX 4.722222
## 243 2000 CHN 4.716049
## 244 2015 NYA 4.716049
## 245 2019 SLN 4.716049
## 246 2016 WAS 4.709877
## 247 2011 SLN 4.703704
## 248 2004 LAN 4.697531
## 249 2005 LAA 4.697531
## 250 2017 SLN 4.697531
## 251 2003 SFN 4.689441
## 252 2009 OAK 4.685185
## 253 2016 TOR 4.685185
## 254 2001 LAN 4.679012
## 255 2006 FLO 4.679012
## 256 2012 COL 4.679012
## 257 2019 SEA 4.679012
## 258 2019 PIT 4.679012
## 259 2006 KCA 4.672840
## 260 2014 DET 4.672840
## 261 2006 SEA 4.666667
## 262 2007 BAL 4.666667
## 263 2010 TOR 4.660494
## 264 2014 COL 4.660494
## 265 2016 DET 4.658385
## 266 2020 HOU 4.650000
## 267 2003 PIT 4.648148
## 268 2007 TOR 4.648148
## 269 2008 ATL 4.648148
## 270 2017 CIN 4.648148
## 271 2000 SDN 4.641975
## 272 2007 CHN 4.641975
## 273 2010 CHA 4.641975
## 274 2016 ARI 4.641975
## 275 2003 FLO 4.635802
## 276 2010 DET 4.635802
## 277 2015 TEX 4.635802
## 278 2006 SFN 4.633540
## 279 2004 CIN 4.629630
## 280 2005 TBA 4.629630
## 281 2008 MIL 4.629630
## 282 2010 MIL 4.629630
## 283 2017 SEA 4.629630
## 284 2002 HOU 4.623457
## 285 2006 CIN 4.623457
## 286 2000 MIN 4.617284
## 287 2012 CHA 4.617284
## 288 2015 BOS 4.617284
## 289 2008 COL 4.611111
## 290 2022 PHI 4.611111
## 291 2001 PHI 4.604938
## 292 2006 WAS 4.604938
## 293 2013 BAL 4.598765
## 294 2013 CLE 4.598765
## 295 2016 BAL 4.592593
## 296 2021 COL 4.590062
## 297 2011 TOR 4.586420
## 298 2017 BAL 4.586420
## 299 2021 OAK 4.586420
## 300 2020 COL 4.583333
## 301 2001 FLO 4.580247
## 302 2005 CHA 4.574074
## 303 2007 OAK 4.574074
## 304 2009 BAL 4.574074
## 305 2001 MIL 4.567901
## 306 2005 COL 4.567901
## 307 2020 BAL 4.566667
## 308 2020 OAK 4.566667
## 309 2002 CLE 4.561728
## 310 2017 OAK 4.561728
## 311 2003 BAL 4.558282
## 312 2009 DET 4.558282
## 313 2000 MON 4.555556
## 314 2010 ATL 4.555556
## 315 2021 MIL 4.555556
## 316 2000 TBA 4.552795
## 317 2002 KCA 4.549383
## 318 2015 COL 4.549383
## 319 2022 HOU 4.549383
## 320 2007 SDN 4.546012
## 321 2003 ANA 4.543210
## 322 2010 SLN 4.543210
## 323 2000 FLO 4.540373
## 324 2000 MIL 4.539877
## 325 2001 CIN 4.537037
## 326 2002 MON 4.537037
## 327 2006 HOU 4.537037
## 328 2007 LAN 4.537037
## 329 2008 PIT 4.537037
## 330 2009 ATL 4.537037
## 331 2011 CIN 4.537037
## 332 2011 COL 4.537037
## 333 2017 DET 4.537037
## 334 2017 NYN 4.537037
## 335 2022 BOS 4.537037
## 336 2012 BOS 4.530864
## 337 2012 ARI 4.530864
## 338 2021 PHI 4.530864
## 339 2013 LAA 4.524691
## 340 2017 ATL 4.518519
## 341 2017 MIL 4.518519
## 342 2006 SDN 4.512346
## 343 2011 ARI 4.512346
## 344 2012 WAS 4.512346
## 345 2006 MIL 4.506173
## 346 2009 SLN 4.506173
## 347 2011 KCA 4.506173
## 348 2001 KCA 4.500000
## 349 2001 ATL 4.500000
## 350 2005 BAL 4.500000
## 351 2014 OAK 4.500000
## 352 2015 HOU 4.500000
## 353 2016 PIT 4.500000
## 354 2019 BAL 4.500000
## 355 2021 MIN 4.500000
## 356 2021 SDN 4.500000
## 357 2020 MIN 4.483333
## 358 2020 ARI 4.483333
## 359 2005 MIL 4.481481
## 360 2012 DET 4.481481
## 361 2019 TOR 4.481481
## 362 2013 TEX 4.478528
## 363 2007 SLN 4.475309
## 364 2016 LAN 4.475309
## 365 2022 MIL 4.475309
## 366 2001 DET 4.469136
## 367 2003 CHN 4.469136
## 368 2007 PIT 4.469136
## 369 2009 CHA 4.469136
## 370 2015 KCA 4.469136
## 371 2016 HOU 4.469136
## 372 2021 WAS 4.469136
## 373 2004 TOR 4.465839
## 374 2005 DET 4.462963
## 375 2007 HOU 4.462963
## 376 2014 TOR 4.462963
## 377 2021 LAA 4.462963
## 378 2005 NYN 4.456790
## 379 2016 MIN 4.456790
## 380 2011 MIL 4.450617
## 381 2004 KCA 4.444444
## 382 2008 ARI 4.444444
## 383 2009 ARI 4.444444
## 384 2015 ARI 4.444444
## 385 2010 FLO 4.438272
## 386 2004 TBA 4.434783
## 387 2004 FLO 4.432099
## 388 2007 MIN 4.432099
## 389 2011 NYN 4.432099
## 390 2012 SFN 4.432099
## 391 2014 LAN 4.432099
## 392 2003 ARI 4.425926
## 393 2005 FLO 4.425926
## 394 2016 LAA 4.425926
## 395 2021 CLE 4.425926
## 396 2008 HOU 4.422360
## 397 2006 CHN 4.419753
## 398 2012 TOR 4.419753
## 399 2016 CIN 4.419753
## 400 2022 SFN 4.419753
## 401 2020 CHN 4.416667
## 402 2003 TBA 4.413580
## 403 2014 MIN 4.413580
## 404 2016 SFN 4.413580
## 405 2002 PHI 4.409938
## 406 2003 MIL 4.407407
## 407 2008 TOR 4.407407
## 408 2002 LAN 4.401235
## 409 2010 ARI 4.401235
## 410 2011 PHI 4.401235
## 411 2012 OAK 4.401235
## 412 2015 BAL 4.401235
## 413 2002 ATL 4.397516
## 414 2019 CHA 4.397516
## 415 2007 ARI 4.395062
## 416 2012 BAL 4.395062
## 417 2013 TOR 4.395062
## 418 2009 CHN 4.391304
## 419 2003 MON 4.388889
## 420 2021 NYA 4.388889
## 421 2020 MIA 4.383333
## 422 2009 WAS 4.382716
## 423 2017 LAA 4.382716
## 424 2002 CIN 4.376543
## 425 2000 PHI 4.370370
## 426 2011 BAL 4.370370
## 427 2011 TBA 4.364198
## 428 2022 TEX 4.364198
## 429 2002 CHN 4.358025
## 430 2007 KCA 4.358025
## 431 2013 COL 4.358025
## 432 2017 CHA 4.358025
## 433 2021 SLN 4.358025
## 434 2014 BAL 4.351852
## 435 2021 CHN 4.351852
## 436 2022 SDN 4.351852
## 437 2008 CIN 4.345679
## 438 2011 CLE 4.345679
## 439 2005 CHN 4.339506
## 440 2015 WAS 4.339506
## 441 2017 KCA 4.333333
## 442 2022 ARI 4.333333
## 443 2005 KCA 4.327160
## 444 2012 MIN 4.327160
## 445 2019 CIN 4.327160
## 446 2008 LAN 4.320988
## 447 2012 ATL 4.320988
## 448 2002 FLO 4.314815
## 449 2003 CLE 4.314815
## 450 2005 SEA 4.314815
## 451 2004 SEA 4.308642
## 452 2013 CIN 4.308642
## 453 2022 CLE 4.308642
## 454 2022 COL 4.308642
## 455 2010 SFN 4.302469
## 456 2012 TBA 4.302469
## 457 2015 PIT 4.302469
## 458 2021 DET 4.302469
## 459 2021 SEA 4.302469
## 460 2005 ARI 4.296296
## 461 2015 MIN 4.296296
## 462 2015 SFN 4.296296
## 463 2022 MIN 4.296296
## 464 2013 TBA 4.294479
## 465 2020 DET 4.293103
## 466 2002 NYN 4.285714
## 467 2003 CIN 4.283951
## 468 2015 OAK 4.283951
## 469 2017 TBA 4.283951
## 470 2015 DET 4.279503
## 471 2007 CHA 4.277778
## 472 2017 TOR 4.277778
## 473 2001 ANA 4.265432
## 474 2006 PIT 4.265432
## 475 2008 KCA 4.265432
## 476 2019 KCA 4.265432
## 477 2017 PHI 4.259259
## 478 2022 SEA 4.259259
## 479 2006 TBA 4.253086
## 480 2015 CHN 4.253086
## 481 2005 HOU 4.251534
## 482 2005 MIN 4.246914
## 483 2013 ATL 4.246914
## 484 2001 BAL 4.240741
## 485 2009 KCA 4.234568
## 486 2014 WAS 4.234568
## 487 2016 CHA 4.234568
## 488 2016 SDN 4.234568
## 489 2021 KCA 4.234568
## 490 2022 CHA 4.234568
## 491 2020 SEA 4.233333
## 492 2005 LAN 4.228395
## 493 2010 CHN 4.228395
## 494 2013 ARI 4.228395
## 495 2004 PIT 4.223602
## 496 2004 NYN 4.222222
## 497 2005 SDN 4.222222
## 498 2012 PHI 4.222222
## 499 2007 SFN 4.216049
## 500 2015 NYN 4.216049
## 501 2014 PIT 4.209877
## 502 2019 SDN 4.209877
## 503 2010 LAA 4.203704
## 504 2005 PIT 4.197531
## 505 2016 NYA 4.197531
## 506 2021 ARI 4.191358
## 507 2003 SDN 4.185185
## 508 2019 SFN 4.185185
## 509 2002 TBA 4.180124
## 510 2010 KCA 4.172840
## 511 2012 KCA 4.172840
## 512 2016 KCA 4.166667
## 513 2022 BAL 4.160494
## 514 2015 CLE 4.155280
## 515 2007 WAS 4.154321
## 516 2009 CIN 4.154321
## 517 2001 TBA 4.148148
## 518 2016 TBA 4.148148
## 519 2008 SEA 4.141975
## 520 2009 NYN 4.141975
## 521 2016 MIL 4.141975
## 522 2016 NYN 4.141975
## 523 2020 SLN 4.137931
## 524 2001 MON 4.135802
## 525 2020 CLE 4.133333
## 526 2020 KCA 4.133333
## 527 2012 CIN 4.129630
## 528 2014 CLE 4.129630
## 529 2017 PIT 4.123457
## 530 2002 BAL 4.117284
## 531 2010 LAN 4.117284
## 532 2011 LAA 4.117284
## 533 2012 CLE 4.117284
## 534 2015 LAN 4.117284
## 535 2020 MIL 4.116667
## 536 2022 TBA 4.111111
## 537 2010 SDN 4.104938
## 538 2014 SFN 4.104938
## 539 2010 OAK 4.092593
## 540 2002 SDN 4.086420
## 541 2015 LAA 4.080247
## 542 2014 CHA 4.074074
## 543 2016 MIA 4.068323
## 544 2021 BAL 4.067901
## 545 2001 PIT 4.055556
## 546 2009 SFN 4.055556
## 547 2022 CHN 4.055556
## 548 2020 CIN 4.050000
## 549 2010 NYN 4.049383
## 550 2013 WAS 4.049383
## 551 2015 SEA 4.049383
## 552 2010 WAS 4.043210
## 553 2015 MIL 4.043210
## 554 2011 CHA 4.037037
## 555 2011 CHN 4.037037
## 556 2016 ATL 4.031056
## 557 2016 OAK 4.030864
## 558 2012 PIT 4.018519
## 559 2012 SDN 4.018519
## 560 2014 KCA 4.018519
## 561 2008 OAK 4.012422
## 562 2012 NYN 4.012346
## 563 2013 NYA 4.012346
## 564 2014 MIL 4.012346
## 565 2015 SDN 4.012346
## 566 2005 SFN 4.006173
## 567 2013 LAN 4.006173
## 568 2011 LAN 4.000000
## 569 2013 KCA 4.000000
## 570 2022 CIN 4.000000
## 571 2015 SLN 3.993827
## 572 2010 CLE 3.987654
## 573 2003 NYN 3.987578
## 574 2011 OAK 3.981481
## 575 2014 MIA 3.981481
## 576 2002 PIT 3.981366
## 577 2008 WAS 3.981366
## 578 2015 TBA 3.975309
## 579 2009 HOU 3.969136
## 580 2001 NYN 3.962963
## 581 2011 ATL 3.956790
## 582 2008 SFN 3.950617
## 583 2009 SEA 3.950617
## 584 2013 MIL 3.950617
## 585 2015 CIN 3.950617
## 586 2022 KCA 3.950617
## 587 2009 PIT 3.950311
## 588 2005 WAS 3.944444
## 589 2017 SFN 3.944444
## 590 2009 SDN 3.938272
## 591 2004 MIL 3.937888
## 592 2008 SDN 3.932099
## 593 2012 LAN 3.932099
## 594 2014 TEX 3.932099
## 595 2021 NYN 3.925926
## 596 2004 MON 3.919753
## 597 2013 PIT 3.913580
## 598 2014 BOS 3.913580
## 599 2014 SEA 3.913580
## 600 2014 NYA 3.907407
## 601 2013 SFN 3.882716
## 602 2014 HOU 3.882716
## 603 2014 NYN 3.882716
## 604 2011 WAS 3.875776
## 605 2002 MIL 3.870370
## 606 2015 PHI 3.864198
## 607 2011 FLO 3.858025
## 608 2021 TEX 3.858025
## 609 2013 SEA 3.851852
## 610 2021 MIA 3.845679
## 611 2022 LAA 3.845679
## 612 2015 CHA 3.839506
## 613 2011 MIN 3.820988
## 614 2012 SEA 3.820988
## 615 2013 NYN 3.820988
## 616 2014 PHI 3.820988
## 617 2014 SLN 3.820988
## 618 2013 SDN 3.814815
## 619 2004 ARI 3.796296
## 620 2011 HOU 3.796296
## 621 2014 ARI 3.796296
## 622 2019 MIA 3.796296
## 623 2013 MIN 3.790123
## 624 2014 CHN 3.790123
## 625 2010 BAL 3.783951
## 626 2012 CHN 3.783951
## 627 2015 MIA 3.783951
## 628 2014 TBA 3.777778
## 629 2010 HOU 3.771605
## 630 2011 PIT 3.765432
## 631 2013 HOU 3.765432
## 632 2013 PHI 3.765432
## 633 2016 PHI 3.765432
## 634 2012 MIA 3.759259
## 635 2021 PIT 3.759259
## 636 2020 TEX 3.733333
## 637 2017 SDN 3.728395
## 638 2022 WAS 3.722222
## 639 2013 CHN 3.716049
## 640 2013 CHA 3.691358
## 641 2014 CIN 3.672840
## 642 2011 SDN 3.660494
## 643 2020 PIT 3.650000
## 644 2003 DET 3.648148
## 645 2022 PIT 3.648148
## 646 2010 PIT 3.623457
## 647 2022 MIA 3.617284
## 648 2019 DET 3.614907
## 649 2012 HOU 3.598765
## 650 2002 DET 3.571429
## 651 2003 LAN 3.543210
## 652 2014 ATL 3.537037
## 653 2015 ATL 3.537037
## 654 2011 SFN 3.518519
## 655 2022 OAK 3.506173
## 656 2022 DET 3.438272
## 657 2011 SEA 3.432099
## 658 2014 SDN 3.302469
## 659 2010 SEA 3.166667
## 660 2013 MIA 3.166667
Insights: Chicago White Sox has scored 6 runs per
game on average in year 2000. Which is the highest for
any team over last 22 seasons in MLB.
Question 5: Lets visualize the distribution of Runs Per Game over
last 22 years.
#Lets visualize the distribution of runs per game from 2000-2022.
mean_RPG = sum(lahman_data$R)/sum(lahman_data$G)
data <- lahman_data|>
group_by(yearID)|>
summarise(Runs_Per_Game = sum(R)/sum(G)) |>
ggplot()+
geom_line(aes(x=yearID,y=Runs_Per_Game))+
geom_hline(yintercept = mean_RPG , color = "red")+
annotate("Text",x=2005,y=4.5,label="Avearge Runs_per_Game - 4.6", color='red')+
ggtitle("Runs per game scored year wise")+
theme_classic()
data
Insights: The average runs per game scored over last 22
years was 4.6. The highest runs per game was seen in early 21st century
and it tend to decrease over the course of time.
Question 6: Lets find the factors which have high
correlation with wins using correlation plots
lahman_data |>
ggplot() +
geom_point(aes(x=R,y=W))+
annotate("rect",xmin=200,xmax=360,ymin=10,ymax=45,alpha=0.2)+
theme_classic()
Note: The bottom left corner are the games played in covid period.
The season was stopped abruptly. So, we will try to remove the covid
year 2020 and try working on correlation.
lahman_data |>
filter(yearID!=2020)|>
ggplot(aes(x=R,y=W))+
geom_point(color='red')+
geom_smooth(method='lm',formula = y ~ x,color='black')+
ggtitle("Correlation between Wins and Runs Scored")+
theme_classic()
Insights: There is a positive correlation between runs
scored and wins in Major League Baseball. Let’s now find out the value
of the r.
#Method to find the correlation between the two numeric values.
cor.test(lahman_data$W,lahman_data$R)
##
## Pearson's product-moment correlation
##
## data: lahman_data$W and lahman_data$R
## t = 31.96, df = 658, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7480862 0.8080990
## sample estimates:
## cor
## 0.7798785
Insights: Value of r is 0.78. There is a strong positive
relationship between Runs scored and wins in major league baseball
games. On similar lines lets find the correlation between runs
allowed and wins.
#Lets try to find the correlation between runs allowed and wins.
lahman_data |>
filter(yearID!=2020)|>
ggplot(aes(x=RA,y=W))+
geom_point(color='red')+
geom_smooth(method='lm',formula = y ~ x,color='black')+
ggtitle("Correlation between Wins and Runs Allowed")+
theme_classic()
Insights: There is a negative correlation between runs allowed and
wins in Major League Baseball.
Question 7: Lets try to visualize the home runs scored by teams in
different divisions (E,W,C) and differentiate them by league wise
(AL,NL).
lahman_data |>
mutate(Division=as.factor(divID),League=as.factor(lgID))|>
ggplot() +
geom_boxplot(mapping = aes(x = Division, y = HR, color = League)) +
scale_color_brewer(palette = "Dark2") +
theme_minimal()
Insights: The ability to hit home runs is quite similar in
all the division wise players in both the leagues. They are mostly
bounded between 100-250 home runs. Box plot is the best option to
visualize the continuous vs categorical variables.