Chess Tournament
Joshua Hummell
2/28/21
Overview
The goal of this project is to turn a chess tournament document into a csv file.
It doesn’t seem like it would be that difficult, but that data currently looks like this:
The project has a few stipulations for what needs to be included in the end file
- Player’s Name - Simply, add in their name
- Player’s State - It’s where we see ‘Num’ on the top title
- Player’s total Number of Points - the numbers that come after the outcome of the tournament
- Player’s Pre-Rating (And Player’s ID) - Simply this particular players pre-rating by using their player ID
- Average Pre Chess Rating of Opponents - For all the opponents, you need to get the score for each person and then divide by the total number of games played, which is a max of seven for this tournament.
We will accomplish this in a series of steps which you can find below.
- Reading and Cleaning the data - we will load the data and make it easy to work with
- Extracting the Data
- Creating a Dataframe
Load the Required Packages
library(stringr)
library(dplyr)
library("kableExtra")Step One
Reading the Data
Since this is a txt file and we want to read it in as a character file and maintain the line structure, so we want to use ‘readLines’.
chess <- readLines("https://raw.githubusercontent.com/jhumms/DATA607/main/chess/tournamentinfo.txt")# Let's check out the top rows
paste(head(chess))## [1] "-----------------------------------------------------------------------------------------"
## [2] " Pair | Player Name |Total|Round|Round|Round|Round|Round|Round|Round| "
## [3] " Num | USCF ID / Rtg (Pre->Post) | Pts | 1 | 2 | 3 | 4 | 5 | 6 | 7 | "
## [4] "-----------------------------------------------------------------------------------------"
## [5] " 1 | GARY HUA |6.0 |W 39|W 21|W 18|W 14|W 7|D 12|D 4|"
## [6] " ON | 15445895 / R: 1794 ->1817 |N:2 |W |B |W |B |W |B |W |"Now, there is one more thing we have to do, and that is remove the header from the chess document since we will be creating our own column names later.
chess <- chess[-(1:4)]And now we are ready for Step Two.
Step Two
Extracting the Data
In this section we are going to use some Regex to get all the data we need.
A quick note, for the Regex formulas, I made frequent use of the RegExr Website.
Player ID
chess_id <- unlist(str_extract_all(unlist(chess), "\\d{1,2}(?=\\s\\|)"))
chess_id <- str_trim(chess_id, side = "right")
chess_id## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15"
## [16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
## [31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44" "45"
## [46] "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64"Player Name
chess_player <- unlist(str_extract_all(unlist(chess), "([[:upper:]]+\\s){2,}"))
chess_player <- str_trim(chess_player, side = "right")
chess_player## [1] "GARY HUA" "DAKSHESH DARURI"
## [3] "ADITYA BAJAJ" "PATRICK H SCHILLING"
## [5] "HANSHI ZUO" "HANSEN SONG"
## [7] "GARY DEE SWATHELL" "EZEKIEL HOUGHTON"
## [9] "STEFANO LEE" "ANVIT RAO"
## [11] "CAMERON WILLIAM MC LEMAN" "KENNETH J TACK"
## [13] "TORRANCE HENRY JR" "BRADLEY SHAW"
## [15] "ZACHARY JAMES HOUGHTON" "MIKE NIKITIN"
## [17] "RONALD GRZEGORCZYK" "DAVID SUNDEEN"
## [19] "DIPANKAR ROY" "JASON ZHENG"
## [21] "DINH DANG BUI" "EUGENE L MCCLURE"
## [23] "ALAN BUI" "MICHAEL R ALDRICH"
## [25] "LOREN SCHWIEBERT" "MAX ZHU"
## [27] "GAURAV GIDWANI" "SOFIA ADINA"
## [29] "CHIEDOZIE OKORIE" "GEORGE AVERY JONES"
## [31] "RISHI SHETTY" "JOSHUA PHILIP MATHEWS"
## [33] "JADE GE" "MICHAEL JEFFERY THOMAS"
## [35] "JOSHUA DAVID LEE" "SIDDHARTH JHA"
## [37] "AMIYATOSH PWNANANDAM" "BRIAN LIU"
## [39] "JOEL R HENDON" "FOREST ZHANG"
## [41] "KYLE WILLIAM MURPHY" "JARED GE"
## [43] "ROBERT GLEN VASEY" "JUSTIN D SCHILLING"
## [45] "DEREK YAN" "JACOB ALEXANDER LAVALLEY"
## [47] "ERIC WRIGHT" "DANIEL KHAIN"
## [49] "MICHAEL J MARTIN" "SHIVAM JHA"
## [51] "TEJAS AYYAGARI" "ETHAN GUO"
## [53] "JOSE C YBARRA" "LARRY HODGE"
## [55] "ALEX KONG" "MARISA RICCI"
## [57] "MICHAEL LU" "VIRAJ MOHILE"
## [59] "SEAN M MC CORMICK" "JULIA SHEN"
## [61] "JEZZEL FARKAS" "ASHWIN BALAJI"
## [63] "THOMAS JOSEPH HOSMER" "BEN LI"Player’s State
chess_state <- unlist(str_extract_all(unlist(chess), "([[:upper:]]){2}\\s(?=\\|)"))
chess_state <- str_trim(chess_state, side = "right")
chess_state## [1] "ON" "MI" "MI" "MI" "MI" "OH" "MI" "MI" "ON" "MI" "MI" "MI" "MI" "MI" "MI"
## [16] "MI" "MI" "MI" "MI" "MI" "ON" "MI" "ON" "MI" "MI" "ON" "MI" "MI" "MI" "ON"
## [31] "MI" "ON" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI"
## [46] "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI" "MI"
## [61] "ON" "MI" "MI" "MI"Player’s Number of Points
chess_points <- unlist(str_extract_all(unlist(chess), "\\d\\.\\d"))
chess_points## [1] "6.0" "6.0" "6.0" "5.5" "5.5" "5.0" "5.0" "5.0" "5.0" "5.0" "4.5" "4.5"
## [13] "4.5" "4.5" "4.5" "4.0" "4.0" "4.0" "4.0" "4.0" "4.0" "4.0" "4.0" "4.0"
## [25] "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5" "3.5"
## [37] "3.5" "3.0" "3.0" "3.0" "3.0" "3.0" "3.0" "3.0" "3.0" "3.0" "2.5" "2.5"
## [49] "2.5" "2.5" "2.5" "2.5" "2.0" "2.0" "2.0" "2.0" "2.0" "2.0" "2.0" "1.5"
## [61] "1.5" "1.0" "1.0" "1.0"Player’s Pre-Rating
chess_rate <- unlist(str_extract_all(unlist(chess), "(?<!\\>\\s)(?<=\\s{1,2}|\\s\\:)(\\d{3,4}(?=\\s|P))"))
chess_rate## [1] "1794" "1553" "1384" "1716" "1655" "1686" "1649" "1641" "1411" "1365"
## [11] "1712" "1663" "1666" "1610" "1220" "1604" "1629" "1600" "1564" "1595"
## [21] "1563" "1555" "1363" "1229" "1745" "1579" "1552" "1507" "1602" "1522"
## [31] "1494" "1441" "1449" "1399" "1438" "1355" "980" "1423" "1436" "1348"
## [41] "1403" "1332" "1283" "1199" "1242" "377" "1362" "1382" "1291" "1056"
## [51] "1011" "935" "1393" "1270" "1186" "1153" "1092" "917" "853" "967"
## [61] "955" "1530" "1175" "1163"Opponents Score
For this one, we need to extract all the values and then insert them into a dataframe that we will later add everything to.
chess_score <- unlist(str_extract_all(unlist(chess), "(\\d{1,}|[[:blank:]]{1})(?=\\|)"))
# Let's convert all blanks to '0'
chess_score[chess_score==" "] <- "0"
#Now create a DataFrame
chess_temp <- data.frame(matrix(chess_score, nrow = 64, byrow = T))
##Now remove the columns without data
chess_temp <- subset(chess_temp, select = 4:10)
# Coerce the characters to numeric
chess_temp <- as.data.frame(sapply(chess_temp, as.numeric))
#Do some final mathStep Three
Creating the Dataframe
First we need to create an intermediate table to get the values together
chess_table <- data.frame(chess_id,chess_player, chess_state, chess_points, chess_rate)
chess_final <- 'NA'
chess_int <- cbind(chess_table, chess_temp)
# Let's rename our columns
colnames(chess_int) <-c("player.id", "player.name", "player.state","player.points", "player.rating","o1","o2","o3","o4","o5","o6","o7")Now, let’s add in the average pre-score value for the oponents.
- We need to make a new key value pair (like a Python dictionary)
- We need to add in the new columns for the values
- We need to match the key value pair with the data frame
- We need to turn the NA’s to 0 in order to do effective math
- Finally, we need to make sure the values are numeric
# Key Value pair
mat <- chess_int %>% select(player.id,player.rating)
# Create new columns
chess_int$v1 <- 0
chess_int$v2 <- 0
chess_int$v3 <- 0
chess_int$v4 <- 0
chess_int$v5 <- 0
chess_int$v6 <- 0
chess_int$v7 <- 0
# Match the values!
chess_int$v1 <- mat$player.rating[match(chess_int$o1, mat$player.id)]
chess_int$v2 <- mat$player.rating[match(chess_int$o2, mat$player.id)]
chess_int$v3 <- mat$player.rating[match(chess_int$o3, mat$player.id)]
chess_int$v4 <- mat$player.rating[match(chess_int$o4, mat$player.id)]
chess_int$v5 <- mat$player.rating[match(chess_int$o5, mat$player.id)]
chess_int$v6 <- mat$player.rating[match(chess_int$o6, mat$player.id)]
chess_int$v7 <- mat$player.rating[match(chess_int$o7, mat$player.id)]
#Handle the NAs
chess_int[is.na(chess_int)] <- 0
# Turn the Values to Numeric
chess_int[, 13:19] <- sapply(chess_int[, 13:19], as.numeric)
# Get the final avg score
chess_int$oponent.avg <- round((rowSums(chess_int[,13:19]) / 7))Finally, let’s create our final table!
chess_final <- chess_int %>% select(player.id,player.name,player.state,player.points, player.rating, oponent.avg)
kable(chess_final, "html") %>% kable_styling("striped") %>% scroll_box(width = "100%", height = "350px")| player.id | player.name | player.state | player.points | player.rating | oponent.avg |
|---|---|---|---|---|---|
| 1 | GARY HUA | ON | 6.0 | 1794 | 1605 |
| 2 | DAKSHESH DARURI | MI | 6.0 | 1553 | 1469 |
| 3 | ADITYA BAJAJ | MI | 6.0 | 1384 | 1564 |
| 4 | PATRICK H SCHILLING | MI | 5.5 | 1716 | 1574 |
| 5 | HANSHI ZUO | MI | 5.5 | 1655 | 1501 |
| 6 | HANSEN SONG | OH | 5.0 | 1686 | 1519 |
| 7 | GARY DEE SWATHELL | MI | 5.0 | 1649 | 1372 |
| 8 | EZEKIEL HOUGHTON | MI | 5.0 | 1641 | 1468 |
| 9 | STEFANO LEE | ON | 5.0 | 1411 | 1523 |
| 10 | ANVIT RAO | MI | 5.0 | 1365 | 1554 |
| 11 | CAMERON WILLIAM MC LEMAN | MI | 4.5 | 1712 | 1468 |
| 12 | KENNETH J TACK | MI | 4.5 | 1663 | 1291 |
| 13 | TORRANCE HENRY JR | MI | 4.5 | 1666 | 1498 |
| 14 | BRADLEY SHAW | MI | 4.5 | 1610 | 1515 |
| 15 | ZACHARY JAMES HOUGHTON | MI | 4.5 | 1220 | 1484 |
| 16 | MIKE NIKITIN | MI | 4.0 | 1604 | 990 |
| 17 | RONALD GRZEGORCZYK | MI | 4.0 | 1629 | 1499 |
| 18 | DAVID SUNDEEN | MI | 4.0 | 1600 | 1480 |
| 19 | DIPANKAR ROY | MI | 4.0 | 1564 | 1426 |
| 20 | JASON ZHENG | MI | 4.0 | 1595 | 1411 |
| 21 | DINH DANG BUI | ON | 4.0 | 1563 | 1470 |
| 22 | EUGENE L MCCLURE | MI | 4.0 | 1555 | 1115 |
| 23 | ALAN BUI | ON | 4.0 | 1363 | 1214 |
| 24 | MICHAEL R ALDRICH | MI | 4.0 | 1229 | 1357 |
| 25 | LOREN SCHWIEBERT | MI | 3.5 | 1745 | 1363 |
| 26 | MAX ZHU | ON | 3.5 | 1579 | 1507 |
| 27 | GAURAV GIDWANI | MI | 3.5 | 1552 | 1047 |
| 28 | SOFIA ADINA | MI | 3.5 | 1507 | 1522 |
| 29 | CHIEDOZIE OKORIE | MI | 3.5 | 1602 | 1126 |
| 30 | GEORGE AVERY JONES | ON | 3.5 | 1522 | 1144 |
| 31 | RISHI SHETTY | MI | 3.5 | 1494 | 1260 |
| 32 | JOSHUA PHILIP MATHEWS | ON | 3.5 | 1441 | 1379 |
| 33 | JADE GE | MI | 3.5 | 1449 | 1277 |
| 34 | MICHAEL JEFFERY THOMAS | MI | 3.5 | 1399 | 1375 |
| 35 | JOSHUA DAVID LEE | MI | 3.5 | 1438 | 1150 |
| 36 | SIDDHARTH JHA | MI | 3.5 | 1355 | 1190 |
| 37 | AMIYATOSH PWNANANDAM | MI | 3.5 | 980 | 989 |
| 38 | BRIAN LIU | MI | 3.0 | 1423 | 1319 |
| 39 | JOEL R HENDON | MI | 3.0 | 1436 | 1430 |
| 40 | FOREST ZHANG | MI | 3.0 | 1348 | 1391 |
| 41 | KYLE WILLIAM MURPHY | MI | 3.0 | 1403 | 713 |
| 42 | JARED GE | MI | 3.0 | 1332 | 1150 |
| 43 | ROBERT GLEN VASEY | MI | 3.0 | 1283 | 1107 |
| 44 | JUSTIN D SCHILLING | MI | 3.0 | 1199 | 1137 |
| 45 | DEREK YAN | MI | 3.0 | 1242 | 1152 |
| 46 | JACOB ALEXANDER LAVALLEY | MI | 3.0 | 377 | 1358 |
| 47 | ERIC WRIGHT | MI | 2.5 | 1362 | 1392 |
| 48 | DANIEL KHAIN | MI | 2.5 | 1382 | 968 |
| 49 | MICHAEL J MARTIN | MI | 2.5 | 1291 | 918 |
| 50 | SHIVAM JHA | MI | 2.5 | 1056 | 1111 |
| 51 | TEJAS AYYAGARI | MI | 2.5 | 1011 | 1356 |
| 52 | ETHAN GUO | MI | 2.5 | 935 | 1495 |
| 53 | JOSE C YBARRA | MI | 2.0 | 1393 | 577 |
| 54 | LARRY HODGE | MI | 2.0 | 1270 | 1034 |
| 55 | ALEX KONG | MI | 2.0 | 1186 | 1205 |
| 56 | MARISA RICCI | MI | 2.0 | 1153 | 1010 |
| 57 | MICHAEL LU | MI | 2.0 | 1092 | 1168 |
| 58 | VIRAJ MOHILE | MI | 2.0 | 917 | 1192 |
| 59 | SEAN M MC CORMICK | MI | 2.0 | 853 | 1131 |
| 60 | JULIA SHEN | MI | 1.5 | 967 | 950 |
| 61 | JEZZEL FARKAS | ON | 1.5 | 955 | 1327 |
| 62 | ASHWIN BALAJI | MI | 1.0 | 1530 | 169 |
| 63 | THOMAS JOSEPH HOSMER | MI | 1.0 | 1175 | 964 |
| 64 | BEN LI | MI | 1.0 | 1163 | 1263 |
Now that we have the final table, let’s print it out as a CSV.
write.csv(chess_final, file = "chess_tournament.csv")Extra Credit
let’s get the results of each match and compare it to the expected outcome of the match
First, let’s get the match results in a nice clean format
The process is described here:
1. Pull all data in between pipes
2. Put that data in a dataframe
3. Remove the unneeded columns
4. Remove numeric data from the matrix and add it back as a dataframe (it was converted from the sapply function) 5. Convert the matrix to a dataframe
6. Trim the white spaces
7. Add in column names
8. Remove every other row since it is data not needed ( we need 64 rows)
# 1
chess_e <- unlist(str_extract_all(unlist(chess), '(?<=\\|)[^|]++(?=\\|)'))
# 2
chess_e <- as.data.frame(matrix(chess_e, ncol = 9, byrow = T))
# 3
chess_e <- subset(chess_e, select = 3:9)
# 4
chess_e <-sapply(chess_e,function(x) gsub("[0-9]","",as.character(x)))
# 5
chess_e <- as.data.frame(chess_e)
# 6
chess_e<- chess_e %>% mutate_all(trimws)
# 7
colnames(chess_e) <-c("r1","r2","r3","r4","r5","r6","r7")
# 8
toDelete <- seq(1, nrow(chess_e), 2)
chess_e <- chess_e[toDelete,]Great, now that we have the data we need, let’s add it to a larger dataframe that has the all the ratings of each player (what we found above) and merge it with this data.
Then we have a couple of things to calculate
1. First, we need to merge the data then we need to do some ELO math to compute the the odds for each game. 2. We need to compare those odds to the results
Before we begin, let’s go over the formula we need for the ELO ranking system.
- How to predict the outcome of a match: 1/ 1+10^((Rb-Ra)/400)
- Basically this is comparing the scores of each player to determine who has the better odds
chess_ec <- chess_int %>% select(player.id, player.rating,v1,v2,v3,v4,v5,v6,v7)
chess_full <- cbind(chess_ec, chess_e)
# Turn the Values to Numeric
chess_full[, 2:9] <- sapply(chess_full[, 2:9], as.numeric)
chess_full$result1 <- 0
chess_full$result2 <- 0
chess_full$result3 <- 0
chess_full$result4 <- 0
chess_full$result5 <- 0
chess_full$result6 <- 0
chess_full$result7 <- 0
# I decided to round it two positions to make it easier to read
chess_full$result1 <- ifelse(chess_full$v1 >0, round((1/ (1+(10^((chess_full$v1-chess_full$player.rating)/400)))*100)),101)
chess_full$result2 <- ifelse(chess_full$v2 >0,round((1/ (1+(10^((chess_full$v2-chess_full$player.rating)/400)))*100)),101)
chess_full$result3 <- ifelse(chess_full$v3 >0,round((1/ (1+(10^((chess_full$v3-chess_full$player.rating)/400)))*100)),101)
chess_full$result4 <- ifelse(chess_full$v4 >0,round((1/ (1+(10^((chess_full$v4-chess_full$player.rating)/400)))*100)),101)
chess_full$result5 <- ifelse(chess_full$v5 >0,round((1/ (1+(10^((chess_full$v5-chess_full$player.rating)/400)))*100)),101)
chess_full$result6 <- ifelse(chess_full$v6 >0,round((1/ (1+(10^((chess_full$v6-chess_full$player.rating)/400)))*100)),101)
chess_full$result7 <- ifelse(chess_full$v7 >0,round((1/ (1+(10^((chess_full$v7-chess_full$player.rating)/400)))*100)),101)
#Now make sure they are numerical
chess_full[, 17:23] <- sapply(chess_full[, 17:23], as.numeric)Now that we have all the data we need, we are going to analyze and compare the results to what was the expected outcome. In order to do that we need to learn a little more about the ELO system.
The rating system is able to tell you the outcome based on the probability of a win + Draw - loss. So for all of these numbers we need to determine which one is higher. There are more complicated ways to show this, but to keep it simple, I will categorize each range of probability (i.e. 0%-10%) by the highest expected outcome.
- 0-30%, there is a higher chance of loss.
- 40-60%, there is a higher chance for a draw.
- 70-100%, there is a higher chance for winning.
Thanks to this Stack Exchange forum for the odds (although not the only distribution, it’s assumption is that the rankings are accurate to the person).
Below, we are going to do several things.
1. Calculate the odds, as described above
2. Convert the results into math (leaving out everything but W, L, D)
3. Compare the the expected outcome vs. actual outcome as Expected, Better, or Worse 4. Count the results and add it back to the main data frame.
chess_full$expect1 <- ifelse(chess_full$result1 < 40, 0,
ifelse(chess_full$result1 <= 60, .5,
ifelse(chess_full$result1 <= 100 ,1, NA)))
chess_full$expect2 <- ifelse(chess_full$result2 < 40, 0,
ifelse(chess_full$result2 <= 60, .5,
ifelse(chess_full$result2 <= 100 ,1, NA)))
chess_full$expect3 <- ifelse(chess_full$result3 < 40, 0,
ifelse(chess_full$result3 <= 60, .5,
ifelse(chess_full$result3 <= 100 ,1, NA)))
chess_full$expect4 <- ifelse(chess_full$result4 < 40, 0,
ifelse(chess_full$result4 <= 60, .5,
ifelse(chess_full$result4 <= 100 ,1, NA)))
chess_full$expect5 <- ifelse(chess_full$result5 < 40, 0,
ifelse(chess_full$result5 <= 60, .5,
ifelse(chess_full$result5 <= 100 ,1, NA)))
chess_full$expect6 <- ifelse(chess_full$result6 < 40, 0,
ifelse(chess_full$result6 <= 60, .5,
ifelse(chess_full$result6 <= 100 ,1, NA)))
chess_full$expect7 <- ifelse(chess_full$result7 < 40, 0,
ifelse(chess_full$result7 <= 60, .5,
ifelse(chess_full$result7 <= 100 ,1, NA)))
##################################################################################################
chess_full$res1 <- ifelse(chess_full$r1 == "W", 1,
ifelse(chess_full$r1 == "D", .5,
ifelse(chess_full$r1 == "L" ,0, NA)))
chess_full$res2 <- ifelse(chess_full$r2 == "W", 1,
ifelse(chess_full$r2 == "D", .5,
ifelse(chess_full$r2 == "L" ,0, NA)))
chess_full$res3 <- ifelse(chess_full$r3 == "W", 1,
ifelse(chess_full$r3 == "D", .5,
ifelse(chess_full$r3 == "L" ,0, NA)))
chess_full$res4 <- ifelse(chess_full$r4 == "W", 1,
ifelse(chess_full$r4 == "D", .5,
ifelse(chess_full$r4 == "L" ,0, NA)))
chess_full$res5 <- ifelse(chess_full$r5 == "W", 1,
ifelse(chess_full$r5 == "D", .5,
ifelse(chess_full$r5 == "L" ,0, NA)))
chess_full$res6 <- ifelse(chess_full$r6 == "W", 1,
ifelse(chess_full$r6 == "D", .5,
ifelse(chess_full$r6 == "L" ,0, NA)))
chess_full$res7 <- ifelse(chess_full$r7 == "W", 1,
ifelse(chess_full$r7 == "D", .5,
ifelse(chess_full$r7 == "L" ,0, NA)))
##################################################################################################
chess_full[, 24:37] <- sapply(chess_full[, 24:37], as.numeric)
chess_full$compare1 <- ifelse(chess_full$res1 == chess_full$expect1, "Expected",
ifelse(chess_full$res1 > chess_full$expect1, "Better","Worse"))
chess_full$compare2 <- ifelse(chess_full$res2 == chess_full$expect2, "Expected",
ifelse(chess_full$res2 > chess_full$expect2, "Better","Worse"))
chess_full$compare3 <- ifelse(chess_full$res3 == chess_full$expect3, "Expected",
ifelse(chess_full$res3 > chess_full$expect3, "Better","Worse"))
chess_full$compare4 <- ifelse(chess_full$res4 == chess_full$expect4, "Expected",
ifelse(chess_full$res4 > chess_full$expect4, "Better","Worse"))
chess_full$compare5 <- ifelse(chess_full$res5 == chess_full$expect5, "Expected",
ifelse(chess_full$res5 > chess_full$expect5, "Better","Worse"))
chess_full$compare6 <- ifelse(chess_full$res6 == chess_full$expect6, "Expected",
ifelse(chess_full$res6 > chess_full$expect6, "Better","Worse"))
chess_full$compare7 <- ifelse(chess_full$res7 == chess_full$expect7, "Expected",
ifelse(chess_full$res7 > chess_full$expect7, "Better","Worse"))
chess_full[, 38:44] <- sapply(chess_full[, 38:44], as.factor)
##################################################################################################
chess_full$player.expected <- apply(chess_full[38:44], 1, function(x) length(which(x=="Expected")))
chess_full$player.better <- apply(chess_full[38:44], 1, function(x) length(which(x=="Better")))
chess_full$player.worse <- apply(chess_full[38:44], 1, function(x) length(which(x=="Worse")))
chess_full[, 45:47] <- sapply(chess_full[, 45:47], as.numeric)
chess_full$perc.expected <- round((chess_full$player.expected / (chess_full$player.better+ chess_full$player.expected+chess_full$player.worse)*100))
chess_full$perc.better <- round((chess_full$player.better / (chess_full$player.better+ chess_full$player.expected+chess_full$player.worse)*100))
chess_full$perc.worse <- round((chess_full$player.worse / (chess_full$player.better+ chess_full$player.expected+chess_full$player.worse)*100))
##################################################################################################
extra_crd <- chess_full %>% select(player.id, player.expected, player.better, player.worse,perc.expected, perc.better, perc.worse)
chess_ec_final <- merge(chess_final,extra_crd,by="player.id")
kable(chess_ec_final, "html") %>% kable_styling("striped") %>% scroll_box(width = "100%", height = "350px")| player.id | player.name | player.state | player.points | player.rating | oponent.avg | player.expected | player.better | player.worse | perc.expected | perc.better | perc.worse |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | GARY HUA | ON | 6.0 | 1794 | 1605 | 5 | 0 | 2 | 71 | 0 | 29 |
| 10 | ANVIT RAO | MI | 5.0 | 1365 | 1554 | 2 | 5 | 0 | 29 | 71 | 0 |
| 11 | CAMERON WILLIAM MC LEMAN | MI | 4.5 | 1712 | 1468 | 3 | 1 | 3 | 43 | 14 | 43 |
| 12 | KENNETH J TACK | MI | 4.5 | 1663 | 1291 | 4 | 1 | 1 | 67 | 17 | 17 |
| 13 | TORRANCE HENRY JR | MI | 4.5 | 1666 | 1498 | 5 | 0 | 2 | 71 | 0 | 29 |
| 14 | BRADLEY SHAW | MI | 4.5 | 1610 | 1515 | 5 | 1 | 1 | 71 | 14 | 14 |
| 15 | ZACHARY JAMES HOUGHTON | MI | 4.5 | 1220 | 1484 | 2 | 5 | 0 | 29 | 71 | 0 |
| 16 | MIKE NIKITIN | MI | 4.0 | 1604 | 990 | 3 | 0 | 2 | 60 | 0 | 40 |
| 17 | RONALD GRZEGORCZYK | MI | 4.0 | 1629 | 1499 | 3 | 1 | 3 | 43 | 14 | 43 |
| 18 | DAVID SUNDEEN | MI | 4.0 | 1600 | 1480 | 5 | 0 | 2 | 71 | 0 | 29 |
| 19 | DIPANKAR ROY | MI | 4.0 | 1564 | 1426 | 5 | 1 | 1 | 71 | 14 | 14 |
| 2 | DAKSHESH DARURI | MI | 6.0 | 1553 | 1469 | 3 | 4 | 0 | 43 | 57 | 0 |
| 20 | JASON ZHENG | MI | 4.0 | 1595 | 1411 | 4 | 0 | 3 | 57 | 0 | 43 |
| 21 | DINH DANG BUI | ON | 4.0 | 1563 | 1470 | 6 | 0 | 1 | 86 | 0 | 14 |
| 22 | EUGENE L MCCLURE | MI | 4.0 | 1555 | 1115 | 3 | 0 | 3 | 50 | 0 | 50 |
| 23 | ALAN BUI | ON | 4.0 | 1363 | 1214 | 7 | 0 | 0 | 100 | 0 | 0 |
| 24 | MICHAEL R ALDRICH | MI | 4.0 | 1229 | 1357 | 4 | 3 | 0 | 57 | 43 | 0 |
| 25 | LOREN SCHWIEBERT | MI | 3.5 | 1745 | 1363 | 3 | 0 | 4 | 43 | 0 | 57 |
| 26 | MAX ZHU | ON | 3.5 | 1579 | 1507 | 4 | 1 | 2 | 57 | 14 | 29 |
| 27 | GAURAV GIDWANI | MI | 3.5 | 1552 | 1047 | 6 | 0 | 0 | 100 | 0 | 0 |
| 28 | SOFIA ADINA | MI | 3.5 | 1507 | 1522 | 4 | 2 | 1 | 57 | 29 | 14 |
| 29 | CHIEDOZIE OKORIE | MI | 3.5 | 1602 | 1126 | 3 | 1 | 2 | 50 | 17 | 33 |
| 3 | ADITYA BAJAJ | MI | 6.0 | 1384 | 1564 | 2 | 5 | 0 | 29 | 71 | 0 |
| 30 | GEORGE AVERY JONES | ON | 3.5 | 1522 | 1144 | 3 | 0 | 4 | 43 | 0 | 57 |
| 31 | RISHI SHETTY | MI | 3.5 | 1494 | 1260 | 3 | 1 | 3 | 43 | 14 | 43 |
| 32 | JOSHUA PHILIP MATHEWS | ON | 3.5 | 1441 | 1379 | 6 | 1 | 0 | 86 | 14 | 0 |
| 33 | JADE GE | MI | 3.5 | 1449 | 1277 | 5 | 0 | 2 | 71 | 0 | 29 |
| 34 | MICHAEL JEFFERY THOMAS | MI | 3.5 | 1399 | 1375 | 4 | 2 | 1 | 57 | 29 | 14 |
| 35 | JOSHUA DAVID LEE | MI | 3.5 | 1438 | 1150 | 3 | 1 | 3 | 43 | 14 | 43 |
| 36 | SIDDHARTH JHA | MI | 3.5 | 1355 | 1190 | 4 | 2 | 0 | 67 | 33 | 0 |
| 37 | AMIYATOSH PWNANANDAM | MI | 3.5 | 980 | 989 | 3 | 2 | 0 | 60 | 40 | 0 |
| 38 | BRIAN LIU | MI | 3.0 | 1423 | 1319 | 2 | 3 | 1 | 33 | 50 | 17 |
| 39 | JOEL R HENDON | MI | 3.0 | 1436 | 1430 | 6 | 0 | 1 | 86 | 0 | 14 |
| 4 | PATRICK H SCHILLING | MI | 5.5 | 1716 | 1574 | 5 | 1 | 1 | 71 | 14 | 14 |
| 40 | FOREST ZHANG | MI | 3.0 | 1348 | 1391 | 6 | 1 | 0 | 86 | 14 | 0 |
| 41 | KYLE WILLIAM MURPHY | MI | 3.0 | 1403 | 713 | 4 | 0 | 0 | 100 | 0 | 0 |
| 42 | JARED GE | MI | 3.0 | 1332 | 1150 | 3 | 0 | 4 | 43 | 0 | 57 |
| 43 | ROBERT GLEN VASEY | MI | 3.0 | 1283 | 1107 | 5 | 0 | 2 | 71 | 0 | 29 |
| 44 | JUSTIN D SCHILLING | MI | 3.0 | 1199 | 1137 | 4 | 1 | 1 | 67 | 17 | 17 |
| 45 | DEREK YAN | MI | 3.0 | 1242 | 1152 | 3 | 1 | 3 | 43 | 14 | 43 |
| 46 | JACOB ALEXANDER LAVALLEY | MI | 3.0 | 377 | 1358 | 4 | 3 | 0 | 57 | 43 | 0 |
| 47 | ERIC WRIGHT | MI | 2.5 | 1362 | 1392 | 6 | 0 | 1 | 86 | 0 | 14 |
| 48 | DANIEL KHAIN | MI | 2.5 | 1382 | 968 | 3 | 0 | 2 | 60 | 0 | 40 |
| 49 | MICHAEL J MARTIN | MI | 2.5 | 1291 | 918 | 3 | 0 | 2 | 60 | 0 | 40 |
| 5 | HANSHI ZUO | MI | 5.5 | 1655 | 1501 | 5 | 2 | 0 | 71 | 29 | 0 |
| 50 | SHIVAM JHA | MI | 2.5 | 1056 | 1111 | 5 | 1 | 0 | 83 | 17 | 0 |
| 51 | TEJAS AYYAGARI | MI | 2.5 | 1011 | 1356 | 4 | 3 | 0 | 57 | 43 | 0 |
| 52 | ETHAN GUO | MI | 2.5 | 935 | 1495 | 3 | 4 | 0 | 43 | 57 | 0 |
| 53 | JOSE C YBARRA | MI | 2.0 | 1393 | 577 | 2 | 0 | 1 | 67 | 0 | 33 |
| 54 | LARRY HODGE | MI | 2.0 | 1270 | 1034 | 3 | 0 | 3 | 50 | 0 | 50 |
| 55 | ALEX KONG | MI | 2.0 | 1186 | 1205 | 5 | 1 | 0 | 83 | 17 | 0 |
| 56 | MARISA RICCI | MI | 2.0 | 1153 | 1010 | 4 | 1 | 0 | 80 | 20 | 0 |
| 57 | MICHAEL LU | MI | 2.0 | 1092 | 1168 | 4 | 1 | 1 | 67 | 17 | 17 |
| 58 | VIRAJ MOHILE | MI | 2.0 | 917 | 1192 | 5 | 1 | 0 | 83 | 17 | 0 |
| 59 | SEAN M MC CORMICK | MI | 2.0 | 853 | 1131 | 5 | 1 | 0 | 83 | 17 | 0 |
| 6 | HANSEN SONG | OH | 5.0 | 1686 | 1519 | 4 | 0 | 3 | 57 | 0 | 43 |
| 60 | JULIA SHEN | MI | 1.5 | 967 | 950 | 3 | 2 | 0 | 60 | 40 | 0 |
| 61 | JEZZEL FARKAS | ON | 1.5 | 955 | 1327 | 4 | 2 | 1 | 57 | 29 | 14 |
| 62 | ASHWIN BALAJI | MI | 1.0 | 1530 | 169 | 1 | 0 | 0 | 100 | 0 | 0 |
| 63 | THOMAS JOSEPH HOSMER | MI | 1.0 | 1175 | 964 | 3 | 1 | 1 | 60 | 20 | 20 |
| 64 | BEN LI | MI | 1.0 | 1163 | 1263 | 4 | 2 | 1 | 57 | 29 | 14 |
| 7 | GARY DEE SWATHELL | MI | 5.0 | 1649 | 1372 | 4 | 2 | 1 | 57 | 29 | 14 |
| 8 | EZEKIEL HOUGHTON | MI | 5.0 | 1641 | 1468 | 5 | 0 | 2 | 71 | 0 | 29 |
| 9 | STEFANO LEE | ON | 5.0 | 1411 | 1523 | 3 | 4 | 0 | 43 | 57 | 0 |
And finally, let’s find out who played as expected, who played better, and who played worse.
Expected
kable(chess_ec_final %>% arrange(desc(perc.expected)), "html") %>% kable_styling("striped") %>% scroll_box(width = "100%", height = "350px") | player.id | player.name | player.state | player.points | player.rating | oponent.avg | player.expected | player.better | player.worse | perc.expected | perc.better | perc.worse |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | ALAN BUI | ON | 4.0 | 1363 | 1214 | 7 | 0 | 0 | 100 | 0 | 0 |
| 27 | GAURAV GIDWANI | MI | 3.5 | 1552 | 1047 | 6 | 0 | 0 | 100 | 0 | 0 |
| 41 | KYLE WILLIAM MURPHY | MI | 3.0 | 1403 | 713 | 4 | 0 | 0 | 100 | 0 | 0 |
| 62 | ASHWIN BALAJI | MI | 1.0 | 1530 | 169 | 1 | 0 | 0 | 100 | 0 | 0 |
| 21 | DINH DANG BUI | ON | 4.0 | 1563 | 1470 | 6 | 0 | 1 | 86 | 0 | 14 |
| 32 | JOSHUA PHILIP MATHEWS | ON | 3.5 | 1441 | 1379 | 6 | 1 | 0 | 86 | 14 | 0 |
| 39 | JOEL R HENDON | MI | 3.0 | 1436 | 1430 | 6 | 0 | 1 | 86 | 0 | 14 |
| 40 | FOREST ZHANG | MI | 3.0 | 1348 | 1391 | 6 | 1 | 0 | 86 | 14 | 0 |
| 47 | ERIC WRIGHT | MI | 2.5 | 1362 | 1392 | 6 | 0 | 1 | 86 | 0 | 14 |
| 50 | SHIVAM JHA | MI | 2.5 | 1056 | 1111 | 5 | 1 | 0 | 83 | 17 | 0 |
| 55 | ALEX KONG | MI | 2.0 | 1186 | 1205 | 5 | 1 | 0 | 83 | 17 | 0 |
| 58 | VIRAJ MOHILE | MI | 2.0 | 917 | 1192 | 5 | 1 | 0 | 83 | 17 | 0 |
| 59 | SEAN M MC CORMICK | MI | 2.0 | 853 | 1131 | 5 | 1 | 0 | 83 | 17 | 0 |
| 56 | MARISA RICCI | MI | 2.0 | 1153 | 1010 | 4 | 1 | 0 | 80 | 20 | 0 |
| 1 | GARY HUA | ON | 6.0 | 1794 | 1605 | 5 | 0 | 2 | 71 | 0 | 29 |
| 13 | TORRANCE HENRY JR | MI | 4.5 | 1666 | 1498 | 5 | 0 | 2 | 71 | 0 | 29 |
| 14 | BRADLEY SHAW | MI | 4.5 | 1610 | 1515 | 5 | 1 | 1 | 71 | 14 | 14 |
| 18 | DAVID SUNDEEN | MI | 4.0 | 1600 | 1480 | 5 | 0 | 2 | 71 | 0 | 29 |
| 19 | DIPANKAR ROY | MI | 4.0 | 1564 | 1426 | 5 | 1 | 1 | 71 | 14 | 14 |
| 33 | JADE GE | MI | 3.5 | 1449 | 1277 | 5 | 0 | 2 | 71 | 0 | 29 |
| 4 | PATRICK H SCHILLING | MI | 5.5 | 1716 | 1574 | 5 | 1 | 1 | 71 | 14 | 14 |
| 43 | ROBERT GLEN VASEY | MI | 3.0 | 1283 | 1107 | 5 | 0 | 2 | 71 | 0 | 29 |
| 5 | HANSHI ZUO | MI | 5.5 | 1655 | 1501 | 5 | 2 | 0 | 71 | 29 | 0 |
| 8 | EZEKIEL HOUGHTON | MI | 5.0 | 1641 | 1468 | 5 | 0 | 2 | 71 | 0 | 29 |
| 12 | KENNETH J TACK | MI | 4.5 | 1663 | 1291 | 4 | 1 | 1 | 67 | 17 | 17 |
| 36 | SIDDHARTH JHA | MI | 3.5 | 1355 | 1190 | 4 | 2 | 0 | 67 | 33 | 0 |
| 44 | JUSTIN D SCHILLING | MI | 3.0 | 1199 | 1137 | 4 | 1 | 1 | 67 | 17 | 17 |
| 53 | JOSE C YBARRA | MI | 2.0 | 1393 | 577 | 2 | 0 | 1 | 67 | 0 | 33 |
| 57 | MICHAEL LU | MI | 2.0 | 1092 | 1168 | 4 | 1 | 1 | 67 | 17 | 17 |
| 16 | MIKE NIKITIN | MI | 4.0 | 1604 | 990 | 3 | 0 | 2 | 60 | 0 | 40 |
| 37 | AMIYATOSH PWNANANDAM | MI | 3.5 | 980 | 989 | 3 | 2 | 0 | 60 | 40 | 0 |
| 48 | DANIEL KHAIN | MI | 2.5 | 1382 | 968 | 3 | 0 | 2 | 60 | 0 | 40 |
| 49 | MICHAEL J MARTIN | MI | 2.5 | 1291 | 918 | 3 | 0 | 2 | 60 | 0 | 40 |
| 60 | JULIA SHEN | MI | 1.5 | 967 | 950 | 3 | 2 | 0 | 60 | 40 | 0 |
| 63 | THOMAS JOSEPH HOSMER | MI | 1.0 | 1175 | 964 | 3 | 1 | 1 | 60 | 20 | 20 |
| 20 | JASON ZHENG | MI | 4.0 | 1595 | 1411 | 4 | 0 | 3 | 57 | 0 | 43 |
| 24 | MICHAEL R ALDRICH | MI | 4.0 | 1229 | 1357 | 4 | 3 | 0 | 57 | 43 | 0 |
| 26 | MAX ZHU | ON | 3.5 | 1579 | 1507 | 4 | 1 | 2 | 57 | 14 | 29 |
| 28 | SOFIA ADINA | MI | 3.5 | 1507 | 1522 | 4 | 2 | 1 | 57 | 29 | 14 |
| 34 | MICHAEL JEFFERY THOMAS | MI | 3.5 | 1399 | 1375 | 4 | 2 | 1 | 57 | 29 | 14 |
| 46 | JACOB ALEXANDER LAVALLEY | MI | 3.0 | 377 | 1358 | 4 | 3 | 0 | 57 | 43 | 0 |
| 51 | TEJAS AYYAGARI | MI | 2.5 | 1011 | 1356 | 4 | 3 | 0 | 57 | 43 | 0 |
| 6 | HANSEN SONG | OH | 5.0 | 1686 | 1519 | 4 | 0 | 3 | 57 | 0 | 43 |
| 61 | JEZZEL FARKAS | ON | 1.5 | 955 | 1327 | 4 | 2 | 1 | 57 | 29 | 14 |
| 64 | BEN LI | MI | 1.0 | 1163 | 1263 | 4 | 2 | 1 | 57 | 29 | 14 |
| 7 | GARY DEE SWATHELL | MI | 5.0 | 1649 | 1372 | 4 | 2 | 1 | 57 | 29 | 14 |
| 22 | EUGENE L MCCLURE | MI | 4.0 | 1555 | 1115 | 3 | 0 | 3 | 50 | 0 | 50 |
| 29 | CHIEDOZIE OKORIE | MI | 3.5 | 1602 | 1126 | 3 | 1 | 2 | 50 | 17 | 33 |
| 54 | LARRY HODGE | MI | 2.0 | 1270 | 1034 | 3 | 0 | 3 | 50 | 0 | 50 |
| 11 | CAMERON WILLIAM MC LEMAN | MI | 4.5 | 1712 | 1468 | 3 | 1 | 3 | 43 | 14 | 43 |
| 17 | RONALD GRZEGORCZYK | MI | 4.0 | 1629 | 1499 | 3 | 1 | 3 | 43 | 14 | 43 |
| 2 | DAKSHESH DARURI | MI | 6.0 | 1553 | 1469 | 3 | 4 | 0 | 43 | 57 | 0 |
| 25 | LOREN SCHWIEBERT | MI | 3.5 | 1745 | 1363 | 3 | 0 | 4 | 43 | 0 | 57 |
| 30 | GEORGE AVERY JONES | ON | 3.5 | 1522 | 1144 | 3 | 0 | 4 | 43 | 0 | 57 |
| 31 | RISHI SHETTY | MI | 3.5 | 1494 | 1260 | 3 | 1 | 3 | 43 | 14 | 43 |
| 35 | JOSHUA DAVID LEE | MI | 3.5 | 1438 | 1150 | 3 | 1 | 3 | 43 | 14 | 43 |
| 42 | JARED GE | MI | 3.0 | 1332 | 1150 | 3 | 0 | 4 | 43 | 0 | 57 |
| 45 | DEREK YAN | MI | 3.0 | 1242 | 1152 | 3 | 1 | 3 | 43 | 14 | 43 |
| 52 | ETHAN GUO | MI | 2.5 | 935 | 1495 | 3 | 4 | 0 | 43 | 57 | 0 |
| 9 | STEFANO LEE | ON | 5.0 | 1411 | 1523 | 3 | 4 | 0 | 43 | 57 | 0 |
| 38 | BRIAN LIU | MI | 3.0 | 1423 | 1319 | 2 | 3 | 1 | 33 | 50 | 17 |
| 10 | ANVIT RAO | MI | 5.0 | 1365 | 1554 | 2 | 5 | 0 | 29 | 71 | 0 |
| 15 | ZACHARY JAMES HOUGHTON | MI | 4.5 | 1220 | 1484 | 2 | 5 | 0 | 29 | 71 | 0 |
| 3 | ADITYA BAJAJ | MI | 6.0 | 1384 | 1564 | 2 | 5 | 0 | 29 | 71 | 0 |
Better
kable(chess_ec_final %>% arrange(desc(perc.better)), "html") %>% kable_styling("striped") %>% scroll_box(width = "100%", height = "350px")| player.id | player.name | player.state | player.points | player.rating | oponent.avg | player.expected | player.better | player.worse | perc.expected | perc.better | perc.worse |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | ANVIT RAO | MI | 5.0 | 1365 | 1554 | 2 | 5 | 0 | 29 | 71 | 0 |
| 15 | ZACHARY JAMES HOUGHTON | MI | 4.5 | 1220 | 1484 | 2 | 5 | 0 | 29 | 71 | 0 |
| 3 | ADITYA BAJAJ | MI | 6.0 | 1384 | 1564 | 2 | 5 | 0 | 29 | 71 | 0 |
| 2 | DAKSHESH DARURI | MI | 6.0 | 1553 | 1469 | 3 | 4 | 0 | 43 | 57 | 0 |
| 52 | ETHAN GUO | MI | 2.5 | 935 | 1495 | 3 | 4 | 0 | 43 | 57 | 0 |
| 9 | STEFANO LEE | ON | 5.0 | 1411 | 1523 | 3 | 4 | 0 | 43 | 57 | 0 |
| 38 | BRIAN LIU | MI | 3.0 | 1423 | 1319 | 2 | 3 | 1 | 33 | 50 | 17 |
| 24 | MICHAEL R ALDRICH | MI | 4.0 | 1229 | 1357 | 4 | 3 | 0 | 57 | 43 | 0 |
| 46 | JACOB ALEXANDER LAVALLEY | MI | 3.0 | 377 | 1358 | 4 | 3 | 0 | 57 | 43 | 0 |
| 51 | TEJAS AYYAGARI | MI | 2.5 | 1011 | 1356 | 4 | 3 | 0 | 57 | 43 | 0 |
| 37 | AMIYATOSH PWNANANDAM | MI | 3.5 | 980 | 989 | 3 | 2 | 0 | 60 | 40 | 0 |
| 60 | JULIA SHEN | MI | 1.5 | 967 | 950 | 3 | 2 | 0 | 60 | 40 | 0 |
| 36 | SIDDHARTH JHA | MI | 3.5 | 1355 | 1190 | 4 | 2 | 0 | 67 | 33 | 0 |
| 28 | SOFIA ADINA | MI | 3.5 | 1507 | 1522 | 4 | 2 | 1 | 57 | 29 | 14 |
| 34 | MICHAEL JEFFERY THOMAS | MI | 3.5 | 1399 | 1375 | 4 | 2 | 1 | 57 | 29 | 14 |
| 5 | HANSHI ZUO | MI | 5.5 | 1655 | 1501 | 5 | 2 | 0 | 71 | 29 | 0 |
| 61 | JEZZEL FARKAS | ON | 1.5 | 955 | 1327 | 4 | 2 | 1 | 57 | 29 | 14 |
| 64 | BEN LI | MI | 1.0 | 1163 | 1263 | 4 | 2 | 1 | 57 | 29 | 14 |
| 7 | GARY DEE SWATHELL | MI | 5.0 | 1649 | 1372 | 4 | 2 | 1 | 57 | 29 | 14 |
| 56 | MARISA RICCI | MI | 2.0 | 1153 | 1010 | 4 | 1 | 0 | 80 | 20 | 0 |
| 63 | THOMAS JOSEPH HOSMER | MI | 1.0 | 1175 | 964 | 3 | 1 | 1 | 60 | 20 | 20 |
| 12 | KENNETH J TACK | MI | 4.5 | 1663 | 1291 | 4 | 1 | 1 | 67 | 17 | 17 |
| 29 | CHIEDOZIE OKORIE | MI | 3.5 | 1602 | 1126 | 3 | 1 | 2 | 50 | 17 | 33 |
| 44 | JUSTIN D SCHILLING | MI | 3.0 | 1199 | 1137 | 4 | 1 | 1 | 67 | 17 | 17 |
| 50 | SHIVAM JHA | MI | 2.5 | 1056 | 1111 | 5 | 1 | 0 | 83 | 17 | 0 |
| 55 | ALEX KONG | MI | 2.0 | 1186 | 1205 | 5 | 1 | 0 | 83 | 17 | 0 |
| 57 | MICHAEL LU | MI | 2.0 | 1092 | 1168 | 4 | 1 | 1 | 67 | 17 | 17 |
| 58 | VIRAJ MOHILE | MI | 2.0 | 917 | 1192 | 5 | 1 | 0 | 83 | 17 | 0 |
| 59 | SEAN M MC CORMICK | MI | 2.0 | 853 | 1131 | 5 | 1 | 0 | 83 | 17 | 0 |
| 11 | CAMERON WILLIAM MC LEMAN | MI | 4.5 | 1712 | 1468 | 3 | 1 | 3 | 43 | 14 | 43 |
| 14 | BRADLEY SHAW | MI | 4.5 | 1610 | 1515 | 5 | 1 | 1 | 71 | 14 | 14 |
| 17 | RONALD GRZEGORCZYK | MI | 4.0 | 1629 | 1499 | 3 | 1 | 3 | 43 | 14 | 43 |
| 19 | DIPANKAR ROY | MI | 4.0 | 1564 | 1426 | 5 | 1 | 1 | 71 | 14 | 14 |
| 26 | MAX ZHU | ON | 3.5 | 1579 | 1507 | 4 | 1 | 2 | 57 | 14 | 29 |
| 31 | RISHI SHETTY | MI | 3.5 | 1494 | 1260 | 3 | 1 | 3 | 43 | 14 | 43 |
| 32 | JOSHUA PHILIP MATHEWS | ON | 3.5 | 1441 | 1379 | 6 | 1 | 0 | 86 | 14 | 0 |
| 35 | JOSHUA DAVID LEE | MI | 3.5 | 1438 | 1150 | 3 | 1 | 3 | 43 | 14 | 43 |
| 4 | PATRICK H SCHILLING | MI | 5.5 | 1716 | 1574 | 5 | 1 | 1 | 71 | 14 | 14 |
| 40 | FOREST ZHANG | MI | 3.0 | 1348 | 1391 | 6 | 1 | 0 | 86 | 14 | 0 |
| 45 | DEREK YAN | MI | 3.0 | 1242 | 1152 | 3 | 1 | 3 | 43 | 14 | 43 |
| 1 | GARY HUA | ON | 6.0 | 1794 | 1605 | 5 | 0 | 2 | 71 | 0 | 29 |
| 13 | TORRANCE HENRY JR | MI | 4.5 | 1666 | 1498 | 5 | 0 | 2 | 71 | 0 | 29 |
| 16 | MIKE NIKITIN | MI | 4.0 | 1604 | 990 | 3 | 0 | 2 | 60 | 0 | 40 |
| 18 | DAVID SUNDEEN | MI | 4.0 | 1600 | 1480 | 5 | 0 | 2 | 71 | 0 | 29 |
| 20 | JASON ZHENG | MI | 4.0 | 1595 | 1411 | 4 | 0 | 3 | 57 | 0 | 43 |
| 21 | DINH DANG BUI | ON | 4.0 | 1563 | 1470 | 6 | 0 | 1 | 86 | 0 | 14 |
| 22 | EUGENE L MCCLURE | MI | 4.0 | 1555 | 1115 | 3 | 0 | 3 | 50 | 0 | 50 |
| 23 | ALAN BUI | ON | 4.0 | 1363 | 1214 | 7 | 0 | 0 | 100 | 0 | 0 |
| 25 | LOREN SCHWIEBERT | MI | 3.5 | 1745 | 1363 | 3 | 0 | 4 | 43 | 0 | 57 |
| 27 | GAURAV GIDWANI | MI | 3.5 | 1552 | 1047 | 6 | 0 | 0 | 100 | 0 | 0 |
| 30 | GEORGE AVERY JONES | ON | 3.5 | 1522 | 1144 | 3 | 0 | 4 | 43 | 0 | 57 |
| 33 | JADE GE | MI | 3.5 | 1449 | 1277 | 5 | 0 | 2 | 71 | 0 | 29 |
| 39 | JOEL R HENDON | MI | 3.0 | 1436 | 1430 | 6 | 0 | 1 | 86 | 0 | 14 |
| 41 | KYLE WILLIAM MURPHY | MI | 3.0 | 1403 | 713 | 4 | 0 | 0 | 100 | 0 | 0 |
| 42 | JARED GE | MI | 3.0 | 1332 | 1150 | 3 | 0 | 4 | 43 | 0 | 57 |
| 43 | ROBERT GLEN VASEY | MI | 3.0 | 1283 | 1107 | 5 | 0 | 2 | 71 | 0 | 29 |
| 47 | ERIC WRIGHT | MI | 2.5 | 1362 | 1392 | 6 | 0 | 1 | 86 | 0 | 14 |
| 48 | DANIEL KHAIN | MI | 2.5 | 1382 | 968 | 3 | 0 | 2 | 60 | 0 | 40 |
| 49 | MICHAEL J MARTIN | MI | 2.5 | 1291 | 918 | 3 | 0 | 2 | 60 | 0 | 40 |
| 53 | JOSE C YBARRA | MI | 2.0 | 1393 | 577 | 2 | 0 | 1 | 67 | 0 | 33 |
| 54 | LARRY HODGE | MI | 2.0 | 1270 | 1034 | 3 | 0 | 3 | 50 | 0 | 50 |
| 6 | HANSEN SONG | OH | 5.0 | 1686 | 1519 | 4 | 0 | 3 | 57 | 0 | 43 |
| 62 | ASHWIN BALAJI | MI | 1.0 | 1530 | 169 | 1 | 0 | 0 | 100 | 0 | 0 |
| 8 | EZEKIEL HOUGHTON | MI | 5.0 | 1641 | 1468 | 5 | 0 | 2 | 71 | 0 | 29 |
Worse
kable(chess_ec_final %>% arrange(desc(perc.worse)), "html") %>% kable_styling("striped") %>% scroll_box(width = "100%", height = "350px")| player.id | player.name | player.state | player.points | player.rating | oponent.avg | player.expected | player.better | player.worse | perc.expected | perc.better | perc.worse |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 25 | LOREN SCHWIEBERT | MI | 3.5 | 1745 | 1363 | 3 | 0 | 4 | 43 | 0 | 57 |
| 30 | GEORGE AVERY JONES | ON | 3.5 | 1522 | 1144 | 3 | 0 | 4 | 43 | 0 | 57 |
| 42 | JARED GE | MI | 3.0 | 1332 | 1150 | 3 | 0 | 4 | 43 | 0 | 57 |
| 22 | EUGENE L MCCLURE | MI | 4.0 | 1555 | 1115 | 3 | 0 | 3 | 50 | 0 | 50 |
| 54 | LARRY HODGE | MI | 2.0 | 1270 | 1034 | 3 | 0 | 3 | 50 | 0 | 50 |
| 11 | CAMERON WILLIAM MC LEMAN | MI | 4.5 | 1712 | 1468 | 3 | 1 | 3 | 43 | 14 | 43 |
| 17 | RONALD GRZEGORCZYK | MI | 4.0 | 1629 | 1499 | 3 | 1 | 3 | 43 | 14 | 43 |
| 20 | JASON ZHENG | MI | 4.0 | 1595 | 1411 | 4 | 0 | 3 | 57 | 0 | 43 |
| 31 | RISHI SHETTY | MI | 3.5 | 1494 | 1260 | 3 | 1 | 3 | 43 | 14 | 43 |
| 35 | JOSHUA DAVID LEE | MI | 3.5 | 1438 | 1150 | 3 | 1 | 3 | 43 | 14 | 43 |
| 45 | DEREK YAN | MI | 3.0 | 1242 | 1152 | 3 | 1 | 3 | 43 | 14 | 43 |
| 6 | HANSEN SONG | OH | 5.0 | 1686 | 1519 | 4 | 0 | 3 | 57 | 0 | 43 |
| 16 | MIKE NIKITIN | MI | 4.0 | 1604 | 990 | 3 | 0 | 2 | 60 | 0 | 40 |
| 48 | DANIEL KHAIN | MI | 2.5 | 1382 | 968 | 3 | 0 | 2 | 60 | 0 | 40 |
| 49 | MICHAEL J MARTIN | MI | 2.5 | 1291 | 918 | 3 | 0 | 2 | 60 | 0 | 40 |
| 29 | CHIEDOZIE OKORIE | MI | 3.5 | 1602 | 1126 | 3 | 1 | 2 | 50 | 17 | 33 |
| 53 | JOSE C YBARRA | MI | 2.0 | 1393 | 577 | 2 | 0 | 1 | 67 | 0 | 33 |
| 1 | GARY HUA | ON | 6.0 | 1794 | 1605 | 5 | 0 | 2 | 71 | 0 | 29 |
| 13 | TORRANCE HENRY JR | MI | 4.5 | 1666 | 1498 | 5 | 0 | 2 | 71 | 0 | 29 |
| 18 | DAVID SUNDEEN | MI | 4.0 | 1600 | 1480 | 5 | 0 | 2 | 71 | 0 | 29 |
| 26 | MAX ZHU | ON | 3.5 | 1579 | 1507 | 4 | 1 | 2 | 57 | 14 | 29 |
| 33 | JADE GE | MI | 3.5 | 1449 | 1277 | 5 | 0 | 2 | 71 | 0 | 29 |
| 43 | ROBERT GLEN VASEY | MI | 3.0 | 1283 | 1107 | 5 | 0 | 2 | 71 | 0 | 29 |
| 8 | EZEKIEL HOUGHTON | MI | 5.0 | 1641 | 1468 | 5 | 0 | 2 | 71 | 0 | 29 |
| 63 | THOMAS JOSEPH HOSMER | MI | 1.0 | 1175 | 964 | 3 | 1 | 1 | 60 | 20 | 20 |
| 12 | KENNETH J TACK | MI | 4.5 | 1663 | 1291 | 4 | 1 | 1 | 67 | 17 | 17 |
| 38 | BRIAN LIU | MI | 3.0 | 1423 | 1319 | 2 | 3 | 1 | 33 | 50 | 17 |
| 44 | JUSTIN D SCHILLING | MI | 3.0 | 1199 | 1137 | 4 | 1 | 1 | 67 | 17 | 17 |
| 57 | MICHAEL LU | MI | 2.0 | 1092 | 1168 | 4 | 1 | 1 | 67 | 17 | 17 |
| 14 | BRADLEY SHAW | MI | 4.5 | 1610 | 1515 | 5 | 1 | 1 | 71 | 14 | 14 |
| 19 | DIPANKAR ROY | MI | 4.0 | 1564 | 1426 | 5 | 1 | 1 | 71 | 14 | 14 |
| 21 | DINH DANG BUI | ON | 4.0 | 1563 | 1470 | 6 | 0 | 1 | 86 | 0 | 14 |
| 28 | SOFIA ADINA | MI | 3.5 | 1507 | 1522 | 4 | 2 | 1 | 57 | 29 | 14 |
| 34 | MICHAEL JEFFERY THOMAS | MI | 3.5 | 1399 | 1375 | 4 | 2 | 1 | 57 | 29 | 14 |
| 39 | JOEL R HENDON | MI | 3.0 | 1436 | 1430 | 6 | 0 | 1 | 86 | 0 | 14 |
| 4 | PATRICK H SCHILLING | MI | 5.5 | 1716 | 1574 | 5 | 1 | 1 | 71 | 14 | 14 |
| 47 | ERIC WRIGHT | MI | 2.5 | 1362 | 1392 | 6 | 0 | 1 | 86 | 0 | 14 |
| 61 | JEZZEL FARKAS | ON | 1.5 | 955 | 1327 | 4 | 2 | 1 | 57 | 29 | 14 |
| 64 | BEN LI | MI | 1.0 | 1163 | 1263 | 4 | 2 | 1 | 57 | 29 | 14 |
| 7 | GARY DEE SWATHELL | MI | 5.0 | 1649 | 1372 | 4 | 2 | 1 | 57 | 29 | 14 |
| 10 | ANVIT RAO | MI | 5.0 | 1365 | 1554 | 2 | 5 | 0 | 29 | 71 | 0 |
| 15 | ZACHARY JAMES HOUGHTON | MI | 4.5 | 1220 | 1484 | 2 | 5 | 0 | 29 | 71 | 0 |
| 2 | DAKSHESH DARURI | MI | 6.0 | 1553 | 1469 | 3 | 4 | 0 | 43 | 57 | 0 |
| 23 | ALAN BUI | ON | 4.0 | 1363 | 1214 | 7 | 0 | 0 | 100 | 0 | 0 |
| 24 | MICHAEL R ALDRICH | MI | 4.0 | 1229 | 1357 | 4 | 3 | 0 | 57 | 43 | 0 |
| 27 | GAURAV GIDWANI | MI | 3.5 | 1552 | 1047 | 6 | 0 | 0 | 100 | 0 | 0 |
| 3 | ADITYA BAJAJ | MI | 6.0 | 1384 | 1564 | 2 | 5 | 0 | 29 | 71 | 0 |
| 32 | JOSHUA PHILIP MATHEWS | ON | 3.5 | 1441 | 1379 | 6 | 1 | 0 | 86 | 14 | 0 |
| 36 | SIDDHARTH JHA | MI | 3.5 | 1355 | 1190 | 4 | 2 | 0 | 67 | 33 | 0 |
| 37 | AMIYATOSH PWNANANDAM | MI | 3.5 | 980 | 989 | 3 | 2 | 0 | 60 | 40 | 0 |
| 40 | FOREST ZHANG | MI | 3.0 | 1348 | 1391 | 6 | 1 | 0 | 86 | 14 | 0 |
| 41 | KYLE WILLIAM MURPHY | MI | 3.0 | 1403 | 713 | 4 | 0 | 0 | 100 | 0 | 0 |
| 46 | JACOB ALEXANDER LAVALLEY | MI | 3.0 | 377 | 1358 | 4 | 3 | 0 | 57 | 43 | 0 |
| 5 | HANSHI ZUO | MI | 5.5 | 1655 | 1501 | 5 | 2 | 0 | 71 | 29 | 0 |
| 50 | SHIVAM JHA | MI | 2.5 | 1056 | 1111 | 5 | 1 | 0 | 83 | 17 | 0 |
| 51 | TEJAS AYYAGARI | MI | 2.5 | 1011 | 1356 | 4 | 3 | 0 | 57 | 43 | 0 |
| 52 | ETHAN GUO | MI | 2.5 | 935 | 1495 | 3 | 4 | 0 | 43 | 57 | 0 |
| 55 | ALEX KONG | MI | 2.0 | 1186 | 1205 | 5 | 1 | 0 | 83 | 17 | 0 |
| 56 | MARISA RICCI | MI | 2.0 | 1153 | 1010 | 4 | 1 | 0 | 80 | 20 | 0 |
| 58 | VIRAJ MOHILE | MI | 2.0 | 917 | 1192 | 5 | 1 | 0 | 83 | 17 | 0 |
| 59 | SEAN M MC CORMICK | MI | 2.0 | 853 | 1131 | 5 | 1 | 0 | 83 | 17 | 0 |
| 60 | JULIA SHEN | MI | 1.5 | 967 | 950 | 3 | 2 | 0 | 60 | 40 | 0 |
| 62 | ASHWIN BALAJI | MI | 1.0 | 1530 | 169 | 1 | 0 | 0 | 100 | 0 | 0 |
| 9 | STEFANO LEE | ON | 5.0 | 1411 | 1523 | 3 | 4 | 0 | 43 | 57 | 0 |