This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
library(Lahman)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(magrittr)
#Q1
head(Pitching)## playerID yearID stint teamID lgID W L G GS CG SHO SV IPouts H ER HR BB
## 1 bechtge01 1871 1 PH1 NA 1 2 3 3 2 0 0 78 43 23 0 11
## 2 brainas01 1871 1 WS3 NA 12 15 30 30 30 0 0 792 361 132 4 37
## 3 fergubo01 1871 1 NY2 NA 0 0 1 0 0 0 0 3 8 3 0 0
## 4 fishech01 1871 1 RC1 NA 4 16 24 24 22 1 0 639 295 103 3 31
## 5 fleetfr01 1871 1 NY2 NA 0 1 1 1 1 0 0 27 20 10 0 3
## 6 flowedi01 1871 1 TRO NA 0 0 1 0 0 0 0 3 1 0 0 0
## SO BAOpp ERA IBB WP HBP BK BFP GF R SH SF GIDP
## 1 1 NA 7.96 NA 7 NA 0 146 0 42 NA NA NA
## 2 13 NA 4.50 NA 7 NA 0 1291 0 292 NA NA NA
## 3 0 NA 27.00 NA 2 NA 0 14 0 9 NA NA NA
## 4 15 NA 4.35 NA 20 NA 0 1080 1 257 NA NA NA
## 5 0 NA 10.00 NA 0 NA 0 57 0 21 NA NA NA
## 6 0 NA 0.00 NA 0 NA 0 3 1 0 NA NA NA#Q2
Q2a <- filter(Pitching, SHO >= 12)
Q2a## playerID yearID stint teamID lgID W L G GS CG SHO SV IPouts H ER HR
## 1 bradlge01 1876 1 SL3 NL 45 19 64 64 63 16 0 1719 470 78 3
## 2 galvipu01 1884 1 BFN NL 46 22 72 72 71 12 0 1909 566 141 23
## 3 morried01 1886 1 PT1 AA 41 20 64 63 63 12 1 1666 455 151 5
## 4 coombja01 1910 1 PHA AL 31 9 45 38 35 13 1 1059 248 51 0
## 5 alexape01 1915 1 PHI NL 31 10 49 42 36 12 3 1129 253 51 3
## 6 alexape01 1916 1 PHI NL 33 12 48 45 38 16 3 1167 323 67 6
## 7 gibsobo01 1968 1 SLN NL 22 9 34 34 28 13 0 914 198 38 11
## BB SO BAOpp ERA IBB WP HBP BK BFP GF R SH SF GIDP
## 1 38 103 NA 1.23 NA 34 NA 0 2269 0 229 NA NA NA
## 2 63 369 NA 1.99 NA 16 NA 0 2554 0 254 NA NA NA
## 3 118 326 NA 2.45 NA 22 7 0 2252 1 244 NA NA NA
## 4 115 224 NA 1.30 NA 10 7 0 1353 6 74 NA NA NA
## 5 64 241 0.19 1.22 NA 2 10 0 1435 7 86 NA NA NA
## 6 50 167 0.23 1.55 NA 3 10 0 1500 3 90 NA NA NA
## 7 62 268 0.18 1.12 6 4 7 0 1161 0 49 NA NA NAQ2b <- filter(Pitching, SHO>=12 & ERA<=2)
Q2b## playerID yearID stint teamID lgID W L G GS CG SHO SV IPouts H ER HR
## 1 bradlge01 1876 1 SL3 NL 45 19 64 64 63 16 0 1719 470 78 3
## 2 galvipu01 1884 1 BFN NL 46 22 72 72 71 12 0 1909 566 141 23
## 3 coombja01 1910 1 PHA AL 31 9 45 38 35 13 1 1059 248 51 0
## 4 alexape01 1915 1 PHI NL 31 10 49 42 36 12 3 1129 253 51 3
## 5 alexape01 1916 1 PHI NL 33 12 48 45 38 16 3 1167 323 67 6
## 6 gibsobo01 1968 1 SLN NL 22 9 34 34 28 13 0 914 198 38 11
## BB SO BAOpp ERA IBB WP HBP BK BFP GF R SH SF GIDP
## 1 38 103 NA 1.23 NA 34 NA 0 2269 0 229 NA NA NA
## 2 63 369 NA 1.99 NA 16 NA 0 2554 0 254 NA NA NA
## 3 115 224 NA 1.30 NA 10 7 0 1353 6 74 NA NA NA
## 4 64 241 0.19 1.22 NA 2 10 0 1435 7 86 NA NA NA
## 5 50 167 0.23 1.55 NA 3 10 0 1500 3 90 NA NA NA
## 6 62 268 0.18 1.12 6 4 7 0 1161 0 49 NA NA NA#Q3
Q3a <- slice(Pitching, 40:50)
Q3a## playerID yearID stint teamID lgID W L G GS CG SHO SV IPouts H ER HR
## 1 woltery01 1872 1 CL1 NA 3 6 12 8 5 0 0 226 115 51 3
## 2 wrighha01 1872 1 BS1 NA 1 0 7 0 0 0 4 77 26 6 0
## 3 zettlge01 1872 1 TRO NA 14 8 25 22 17 2 1 563 207 45 2
## 4 zettlge01 1872 2 BR1 NA 1 8 9 9 8 0 0 226 106 30 1
## 5 bechtge01 1873 1 PH2 NA 0 2 3 2 1 0 0 48 27 8 0
## 6 brainas01 1873 1 BL1 NA 5 7 14 14 12 0 0 326 182 50 0
## 7 brittji01 1873 1 BR2 NA 17 36 54 54 51 1 0 1442 696 218 6
## 8 campbhu01 1873 1 ELI NA 2 16 19 18 18 0 0 495 251 54 6
## 9 cummica01 1873 1 BL1 NA 28 14 42 42 42 1 0 1146 475 119 4
## 10 fergubo01 1873 1 BR2 NA 0 1 4 1 1 0 0 58 41 12 2
## 11 fishech01 1873 1 PH1 NA 3 4 13 5 5 0 2 253 90 17 1
## BB SO BAOpp ERA IBB WP HBP BK BFP GF R SH SF GIDP
## 1 7 4 NA 6.09 NA 6 NA 0 385 5 106 NA NA NA
## 2 0 1 NA 2.10 NA 0 NA 0 109 6 12 NA NA NA
## 3 8 17 NA 2.16 NA 3 NA 0 836 4 132 NA NA NA
## 4 6 8 NA 3.58 NA 6 NA 0 360 0 62 NA NA NA
## 5 2 0 NA 4.50 NA 0 NA 0 87 1 24 NA NA NA
## 6 9 3 NA 4.14 NA 3 NA 0 568 0 139 NA NA NA
## 7 41 16 NA 4.08 NA 19 NA 0 2353 0 519 NA NA NA
## 8 8 7 NA 2.95 NA 3 NA 0 852 1 213 NA NA NA
## 9 33 34 NA 2.80 NA 10 NA 0 1768 0 292 NA NA NA
## 10 2 0 NA 5.59 NA 0 NA 0 109 3 30 NA NA NA
## 11 10 14 NA 1.81 NA 6 NA 0 400 8 73 NA NA NAQ3b <- Q3a %>% select(playerID, SHO, ERA)
Q3b## playerID SHO ERA
## 1 woltery01 0 6.09
## 2 wrighha01 0 2.10
## 3 zettlge01 2 2.16
## 4 zettlge01 0 3.58
## 5 bechtge01 0 4.50
## 6 brainas01 0 4.14
## 7 brittji01 1 4.08
## 8 campbhu01 0 2.95
## 9 cummica01 1 2.80
## 10 fergubo01 0 5.59
## 11 fishech01 0 1.81Q3c <- Q3a %>% mutate(SOBB = SO/BB) %>% select(playerID, SHO, ERA, SOBB)
Q3c## playerID SHO ERA SOBB
## 1 woltery01 0 6.09 0.5714286
## 2 wrighha01 0 2.10 Inf
## 3 zettlge01 2 2.16 2.1250000
## 4 zettlge01 0 3.58 1.3333333
## 5 bechtge01 0 4.50 0.0000000
## 6 brainas01 0 4.14 0.3333333
## 7 brittji01 1 4.08 0.3902439
## 8 campbhu01 0 2.95 0.8750000
## 9 cummica01 1 2.80 1.0303030
## 10 fergubo01 0 5.59 0.0000000
## 11 fishech01 0 1.81 1.4000000Q3d <- bind_cols(Q3b,Q3c)## New names:
## * playerID -> playerID...1
## * SHO -> SHO...2
## * ERA -> ERA...3
## * playerID -> playerID...4
## * SHO -> SHO...5
## * ...Q3d## playerID...1 SHO...2 ERA...3 playerID...4 SHO...5 ERA...6 SOBB
## 1 woltery01 0 6.09 woltery01 0 6.09 0.5714286
## 2 wrighha01 0 2.10 wrighha01 0 2.10 Inf
## 3 zettlge01 2 2.16 zettlge01 2 2.16 2.1250000
## 4 zettlge01 0 3.58 zettlge01 0 3.58 1.3333333
## 5 bechtge01 0 4.50 bechtge01 0 4.50 0.0000000
## 6 brainas01 0 4.14 brainas01 0 4.14 0.3333333
## 7 brittji01 1 4.08 brittji01 1 4.08 0.3902439
## 8 campbhu01 0 2.95 campbhu01 0 2.95 0.8750000
## 9 cummica01 1 2.80 cummica01 1 2.80 1.0303030
## 10 fergubo01 0 5.59 fergubo01 0 5.59 0.0000000
## 11 fishech01 0 1.81 fishech01 0 1.81 1.4000000#BQ
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 PH1Teams.new <- Teams %>%
mutate(WP_BPT = R^2 / (R^2 + RA^2),
WP = W/G,
Diff = WP_BPT - WP)
S15 <- Teams.new %>% filter(yearID == 2015)
S15## yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin LgWin
## 1 2015 NL ARI ARI W 3 162 81 79 83 N N N
## 2 2015 NL ATL ATL E 4 162 81 67 95 N N N
## 3 2015 AL BAL BAL E 3 162 78 81 81 N N N
## 4 2015 AL BOS BOS E 5 162 81 78 84 N N N
## 5 2015 AL CHA CHW C 4 162 81 76 86 N N N
## 6 2015 NL CHN CHC C 3 162 81 97 65 N Y N
## 7 2015 NL CIN CIN C 5 162 81 64 98 N N N
## 8 2015 AL CLE CLE C 3 161 80 81 80 N N N
## 9 2015 NL COL COL W 5 162 81 68 94 N N N
## 10 2015 AL DET DET C 5 161 81 74 87 N N N
## 11 2015 AL HOU HOU W 2 162 81 86 76 N Y N
## 12 2015 AL KCA KCR C 1 162 81 95 67 Y N Y
## 13 2015 AL LAA ANA W 3 162 81 85 77 N N N
## 14 2015 NL LAN LAD W 1 162 81 92 70 Y N N
## 15 2015 NL MIA FLA E 3 162 81 71 91 N N N
## 16 2015 NL MIL MIL C 4 162 81 68 94 N N N
## 17 2015 AL MIN MIN C 2 162 81 83 79 N N N
## 18 2015 AL NYA NYY E 2 162 81 87 75 N Y N
## 19 2015 NL NYN NYM E 1 162 81 90 72 Y N Y
## 20 2015 AL OAK OAK W 5 162 81 68 94 N N N
## 21 2015 NL PHI PHI E 5 162 81 63 99 N N N
## 22 2015 NL PIT PIT C 2 162 81 98 64 N Y N
## 23 2015 NL SDN SDP W 4 162 81 74 88 N N N
## 24 2015 AL SEA SEA W 4 162 81 76 86 N N N
## 25 2015 NL SFN SFG W 2 162 81 84 78 N N N
## 26 2015 NL SLN STL C 1 162 81 100 62 Y N N
## 27 2015 AL TBA TBD E 4 162 84 80 82 N N N
## 28 2015 AL TEX TEX W 1 162 81 88 74 Y N N
## 29 2015 AL TOR TOR E 1 162 81 93 69 Y N N
## 30 2015 NL WAS WSN E 2 162 81 83 79 N N N
## WSWin R AB H X2B X3B HR BB SO SB CS HBP SF RA ER ERA CG SHO
## 1 N 720 5649 1494 289 48 154 490 1312 132 44 33 57 713 659 4.04 1 12
## 2 N 573 5420 1361 251 18 100 471 1107 69 33 44 31 760 698 4.41 3 10
## 3 N 713 5485 1370 246 20 217 418 1331 44 25 51 32 693 646 4.05 0 10
## 4 N 748 5640 1495 294 33 161 478 1148 71 27 46 42 753 694 4.31 3 10
## 5 N 622 5533 1381 260 27 136 404 1231 68 42 65 37 701 643 3.98 7 9
## 6 N 689 5491 1341 272 30 171 567 1518 95 37 74 35 608 546 3.36 6 21
## 7 N 640 5571 1382 257 27 167 496 1255 134 38 42 40 754 700 4.33 2 8
## 8 N 669 5439 1395 303 29 141 533 1157 86 28 39 50 640 584 3.67 11 10
## 9 N 737 5572 1479 274 49 186 388 1283 97 43 33 34 844 799 5.04 4 4
## 10 N 689 5605 1515 289 49 151 455 1259 83 51 41 35 803 746 4.64 7 12
## 11 N 729 5459 1363 278 26 230 486 1392 121 48 56 43 618 572 3.57 5 13
## 12 Y 724 5575 1497 300 42 139 383 973 104 34 77 47 641 601 3.73 2 8
## 13 N 661 5417 1331 243 21 176 435 1150 52 34 58 40 675 630 3.94 2 12
## 14 N 667 5385 1346 263 26 187 563 1258 59 34 60 30 595 553 3.44 6 21
## 15 N 613 5463 1420 236 40 120 375 1150 112 45 39 40 678 638 4.02 0 12
## 16 N 655 5480 1378 274 34 145 412 1299 84 29 41 34 737 682 4.28 1 7
## 17 N 696 5467 1349 277 44 156 439 1264 70 38 40 41 700 653 4.07 2 12
## 18 N 764 5567 1397 272 19 212 554 1227 63 25 63 54 698 652 4.03 3 4
## 19 N 683 5527 1351 295 17 177 488 1290 51 25 68 32 613 557 3.43 1 14
## 20 N 694 5600 1405 277 46 146 475 1119 78 29 40 38 729 664 4.14 5 15
## 21 N 626 5529 1374 272 37 130 387 1274 88 32 54 29 809 749 4.69 1 7
## 22 N 697 5631 1462 292 27 140 461 1322 98 45 89 41 596 532 3.21 0 13
## 23 N 650 5457 1324 260 36 148 426 1327 82 29 40 42 731 655 4.09 1 6
## 24 N 656 5544 1379 262 22 198 478 1336 69 45 36 35 726 677 4.16 6 12
## 25 N 696 5565 1486 288 39 136 457 1159 93 36 49 37 627 597 3.72 7 18
## 26 N 647 5484 1386 288 39 137 506 1267 69 38 66 42 525 478 2.94 1 15
## 27 N 644 5485 1383 278 32 167 436 1310 87 45 84 47 642 604 3.74 1 12
## 28 N 751 5511 1419 279 32 172 503 1233 101 39 76 54 733 680 4.24 5 9
## 29 N 891 5509 1480 308 17 232 570 1151 88 23 54 62 670 609 3.80 7 10
## 30 N 703 5428 1363 265 13 177 539 1344 57 23 44 51 635 577 3.62 4 13
## SV IPouts HA HRA BBA SOA E DP FP name
## 1 44 4400 1450 182 500 1215 86 146 0.986 Arizona Diamondbacks
## 2 44 4276 1462 170 550 1148 90 186 0.985 Atlanta Braves
## 3 43 4304 1406 174 483 1233 77 134 0.987 Baltimore Orioles
## 4 40 4345 1486 178 478 1218 97 148 0.984 Boston Red Sox
## 5 37 4358 1443 162 474 1359 101 159 0.983 Chicago White Sox
## 6 48 4384 1276 134 407 1431 111 120 0.982 Chicago Cubs
## 7 35 4360 1436 177 544 1252 90 131 0.985 Cincinnati Reds
## 8 38 4298 1274 161 425 1407 79 136 0.987 Cleveland Indians
## 9 36 4279 1579 183 579 1112 95 171 0.985 Colorado Rockies
## 10 35 4341 1491 193 489 1100 86 165 0.986 Detroit Tigers
## 11 39 4323 1308 148 423 1280 85 131 0.986 Houston Astros
## 12 56 4356 1372 155 489 1160 88 138 0.985 Kansas City Royals
## 13 46 4322 1355 166 466 1221 93 108 0.984 Los Angeles Angels of Anaheim
## 14 47 4337 1317 145 395 1396 75 133 0.988 Los Angeles Dodgers
## 15 35 4281 1374 141 508 1152 77 162 0.987 Miami Marlins
## 16 40 4305 1432 176 517 1260 116 164 0.981 Milwaukee Brewers
## 17 45 4329 1506 163 413 1046 86 150 0.986 Minnesota Twins
## 18 48 4373 1416 182 474 1370 93 135 0.985 New York Yankees
## 19 50 4388 1341 152 383 1337 88 131 0.986 New York Mets
## 20 28 4334 1402 172 474 1179 126 154 0.979 Oakland Athletics
## 21 35 4309 1592 191 488 1153 117 145 0.981 Philadelphia Phillies
## 22 54 4469 1392 110 453 1338 122 177 0.981 Pittsburgh Pirates
## 23 41 4321 1371 171 516 1393 92 138 0.985 San Diego Padres
## 24 45 4389 1430 181 491 1283 94 155 0.985 Seattle Mariners
## 25 41 4333 1344 155 431 1165 78 145 0.987 San Francisco Giants
## 26 62 4394 1359 123 477 1329 96 159 0.984 St. Louis Cardinals
## 27 60 4360 1314 175 477 1355 95 118 0.984 Tampa Bay Rays
## 28 45 4328 1459 171 508 1095 119 169 0.981 Texas Rangers
## 29 34 4323 1353 173 397 1117 88 145 0.985 Toronto Blue Jays
## 30 41 4304 1366 145 364 1342 90 125 0.985 Washington Nationals
## park attendance BPF PPF teamIDBR teamIDlahman45
## 1 Chase Field 2080145 107 106 ARI ARI
## 2 Turner Field 2001392 97 97 ATL ATL
## 3 Oriole Park at Camden Yards 2281202 103 104 BAL BAL
## 4 Fenway Park II 2880694 104 107 BOS BOS
## 5 U.S. Cellular Field 1755810 92 93 CHW CHA
## 6 Wrigley Field 2919122 100 100 CHC CHN
## 7 Great American Ball Park 2419506 101 101 CIN CIN
## 8 Progressive Field 1388905 106 106 CLE CLE
## 9 Coors Field 2506789 119 118 COL COL
## 10 Comerica Park 2726048 97 98 DET DET
## 11 Minute Maid Park 2153585 97 99 HOU HOU
## 12 Kauffman Stadium 2708549 104 103 KCR KCA
## 13 Angel Stadium of Anaheim 3012765 94 95 LAA ANA
## 14 Dodger Stadium 3764815 101 98 LAD LAN
## 15 Marlins Park 1752235 98 97 MIA FLO
## 16 Miller Park 2542558 101 101 MIL ML4
## 17 Target Field 2220054 103 104 MIN MIN
## 18 Yankee Stadium III 3193795 99 101 NYY NYA
## 19 Citi Field 2569753 94 92 NYM NYN
## 20 O.co Coliseum 1768175 97 98 OAK OAK
## 21 Citizens Bank Park 1831080 98 98 PHI PHI
## 22 PNC Park 2498596 99 97 PIT PIT
## 23 Petco Park 2459742 98 97 SDP SDN
## 24 Safeco Field 2193581 92 94 SEA SEA
## 25 AT&T Park 3375882 99 97 SFG SFN
## 26 Busch Stadium III 3520889 102 101 STL SLN
## 27 Tropicana Field 1287054 100 102 TBR TBA
## 28 Rangers Ballpark in Arlington 2491875 102 105 TEX TEX
## 29 Rogers Centre 2794891 99 98 TOR TOR
## 30 Nationals Park 2619843 102 99 WSN MON
## teamIDretro WP_BPT WP Diff
## 1 ARI 0.5048847 0.4876543 0.017230419
## 2 ATL 0.3624224 0.4135802 -0.051157805
## 3 BAL 0.5142219 0.5000000 0.014221873
## 4 BOS 0.4966689 0.4814815 0.015187443
## 5 CHA 0.4404994 0.4691358 -0.028636420
## 6 CHN 0.5622092 0.5987654 -0.036556250
## 7 CIN 0.4187642 0.3950617 0.023702508
## 8 CLE 0.5221434 0.5031056 0.019037858
## 9 COL 0.4326299 0.4197531 0.012876812
## 10 DET 0.4240360 0.4596273 -0.035591350
## 11 HOU 0.5818495 0.5308642 0.050985337
## 12 KCA 0.5605819 0.5864198 -0.025837885
## 13 ANA 0.4895221 0.5246914 -0.035169249
## 14 LAN 0.5568672 0.5679012 -0.011034037
## 15 MIA 0.4497787 0.4382716 0.011507138
## 16 MIL 0.4412957 0.4197531 0.021542581
## 17 MIN 0.4971347 0.5123457 -0.015210985
## 18 NYA 0.5450518 0.5370370 0.008014789
## 19 NYN 0.5538552 0.5555556 -0.001700324
## 20 OAK 0.4754189 0.4197531 0.055665860
## 21 PHI 0.3745146 0.3888889 -0.014374263
## 22 PIT 0.5776392 0.6049383 -0.027299081
## 23 SDN 0.4415479 0.4567901 -0.015242187
## 24 SEA 0.4494784 0.4691358 -0.019657417
## 25 SFN 0.5520127 0.5185185 0.033494199
## 26 SLN 0.6029797 0.6172840 -0.014304264
## 27 TBA 0.5015552 0.4938272 0.007728046
## 28 TEX 0.5121276 0.5432099 -0.031082281
## 29 TOR 0.6387940 0.5740741 0.064719889
## 30 WAS 0.5506912 0.5123457 0.038345514```