Earlier in the week we looked at how to use a `for`

loop to iterate across rows of a dataframe to calculate statistics in an automated manner. Interesting and useful, but we only looked at one specific set of circumstance; trainer and jockey combinations in Group races. There are many other useful statistics which can be used to examine a race. This article focuses on today’s Diamond Jubilee Stakes at Royal Ascot, extends the one collection of statistics to four and finally plots the outcome in a visual format.

As the code examples for this article now extend to beyond 550 lines, it is not practicle to include all the code in-line with the article text. Therefore, only certain examples will be included in-line with the full R code will be provided at the end of the article.

The initial assumption is that data has been returned from the Smartform database, although some additional field are now returned, specifically `trainer_id`

, `jockey_id`

and `sire_name`

.

```
# Select relevant historic results
sql1 <- paste("SELECT historic_races.course,
historic_races.meeting_date,
historic_races.conditions,
historic_races.group_race,
historic_races.race_type_id,
historic_races.race_type,
historic_races.distance_yards,
historic_runners.name,
historic_runners.jockey_name,
historic_runners.trainer_name,
historic_runners.finish_position,
historic_runners.starting_price_decimal,
historic_runners.trainer_id,
historic_runners.jockey_id,
historic_runners.sire_name
FROM smartform.historic_runners
JOIN smartform.historic_races USING (race_id)
WHERE historic_races.meeting_date >= '2012-01-01'", sep="")
```

Previously we created a trainer & jockey function to investigate these specific combinations in Group races. This is now extended to just trainer, just jockey and just sire functions. The trainer function is found below.

```
# Trainer stats
# Name the function and add some arguments
tr <- function(race_filter = "", price_filter = 1000, trainer){
# Filter for flat races only
flat_races_only <- dplyr::filter(smartform_results,
race_type_id == 12 |
race_type_id == 15)
# Add an if else statement for the race_filter argument
if (race_filter == "group"){
filtered_races <- dplyr::filter(flat_races_only,
group_race == 1 |
group_race == 2 |
group_race == 3 )
} else {
filtered_races = flat_races_only
}
# Filter by trainer id
trainer_filtered <- dplyr::filter(filtered_races,
grepl(trainer, trainer_id))
# Filter by price
trainer_price_filtered <- dplyr::filter(trainer_filtered,
starting_price_decimal <= price_filter)
# Calculate Profit and Loss
trainer_cumulative <- cumsum(
ifelse(trainer_price_filtered$finish_position == 1,
(trainer_price_filtered$starting_price_decimal-1),
-1)
)
# Calculate Strike Rate
winners <- nrow(dplyr::filter(trainer_price_filtered,
finish_position == 1))
runners <- nrow(trainer_price_filtered)
strike_rate <- (winners / runners) * 100
# Calculate Profit on Turnover or Yield
profit_on_turnover <- (tail(trainer_cumulative, n=1) / runners) * 100
# Check if POT is zero length to catch later errors
if (length(profit_on_turnover) == 0) profit_on_turnover <- 0
# Calculate Impact Values
# First filter all runners by price, to return those just starting at the price_filter or less
all_runners <- nrow(dplyr::filter(filtered_races,
starting_price_decimal <= price_filter))
# Filter all winners by the price filter
all_winners <- nrow(dplyr::filter(filtered_races,
finish_position == 1 &
starting_price_decimal <= price_filter))
# Now calculate the Impact Value
iv <- (winners / all_winners) / (runners / all_runners)
# Calculate Actual vs Expected ratio
# # Convert all decimal odds to probabilities
total_sp <- sum(1/trainer_price_filtered$starting_price_decimal)
# Calculate A/E by dividing the number of winners, by the sum of all SP probabilities.
ae <- winners / total_sp
# Calculate Archie
archie <- (runners * (winners - total_sp)^2)/ (total_sp * (runners - total_sp))
# Calculate the Confidence figure
conf <- pchisq(archie, df = 1)*100
# Create an empty variable
trainer <- NULL
# Add all calculated figures as named objects to the variable, which creates a list
trainer$tr_runners <- runners
trainer$tr_winners <- winners
trainer$tr_sr <- strike_rate
trainer$tr_pot <- profit_on_turnover
trainer$tr_iv <- iv
trainer$tr_ae <- ae
trainer$tr_conf <- conf
# Add an error check to convert all NaN values to zero
final_results <- unlist(trainer)
final_results[ is.nan(final_results) ] <- 0
# Manipulate the layout of returned results to be a nice dataframe
final_results <- t(as.data.frame(final_results))
rownames(final_results) <- c()
# 2 decimal places only
round(final_results, 2)
# Finally, close the function
}
```

Note that in the above code, instead of filtering by `trainer_name`

, we are now filtering by `trainer_id`

. This is due to the fact that sometimes the trainer names in the daily racing data do not exactly match those in the historic data. For example, Sir Michael Stoute hasn’t always been a knight. Therefore, if we were just matching on `trainer_name`

there would be some occassions where this fails and no results are returned. Smartform instead provides a unique identification number for trainers and jockeys, which insures there will always be a match between historic and daily data.

The function above is just one example. In order to produce the charts later in this article, additional jockey and sire functions have been added, bringing the total to four; trainer, jockey, trainer & jockey and sire. The number of statistics could be extended much further to include angles such as trainer & distance, trainer & course, trainer & age (2yo, 3yo, 4yo+ races) and many more.

The `for`

loop also now includes all four of these functions.

```
# Create placeholder lists which will be required later
row_tr <- list()
row_jc <- list()
row_tj <- list()
row_sr <- list()
# Setup the loop
# For each horse in the group_races_only dataframe
for (i in group_races_only$name) {
runner_details = group_races_only[group_races_only$name==i,]
# Extract trainer, jockey id and sire names
trainer <- runner_details$trainer_id
jockey <- runner_details$jockey_id
sire <- runner_details$sire_name
# Apply the Trainer function for Group races only
trainer_combo <- tr(race_filter = "group",
trainer = trainer)
# Add results row by row to the previously defined list
row_tr[[i]] <- trainer_combo
# Apply the Jockey function for Group races only
jockey_combo <- jc(race_filter = "group",
jockey = jockey)
# Add results row by row to the previously defined list
row_jc[[i]] <- jockey_combo
# Apply the Trainer/Jockey function for Group races only
trainer_jockey_combo <- tj(race_filter = "group",
trainer = trainer, jockey = jockey)
# Add results row by row to the previously defined list
row_tj[[i]] <- trainer_jockey_combo
# Apply the Sire function for Group races only
sire_combo <- sr(race_filter = "group",
sire = sire)
# Add results row by row to the previously defined list
row_sr[[i]] <- sire_combo
# Create a final dataframe
stats_final_tr <- as.data.frame(do.call("rbind", row_tr))
stats_final_jc <- as.data.frame(do.call("rbind", row_jc))
stats_final_tj <- as.data.frame(do.call("rbind", row_tj))
stats_final_sr <- as.data.frame(do.call("rbind", row_sr))
}
# Create a new variable called racecard. Bind together the generic race details with the newly created stats
racecard <- cbind(group_races_only,stats_final_tr)
racecard <- cbind(racecard,stats_final_jc)
racecard <- cbind(racecard,stats_final_tj)
racecard <- cbind(racecard,stats_final_sr)
```

Viewing the final `racecard`

now shows forty columns and a wall of data. This isn’t perhaps the easiest way to visualise the overall picture. Instead, we’ll create a stacked barchart showing Impact Values for all four angles. The legend shows `tr_iv`

, `jc_iv`

, `tj_iv`

and `sr_iv`

for the trainer, jockey, trainer & jockey and sire impact values.

```
# Filter for Diamond Jubilee Only
diamond_jubilee <- dplyr::filter(racecard,
grepl("Diamond Jubilee",
race_title))
# Filter for just the IV columns which we will plot
racecard_filtered_iv <- diamond_jubilee[,c("name","tr_iv","jc_iv", "tj_iv", "sr_iv")]
# Convert the racecard from wide to long format
racecard_long_iv <- melt(racecard_filtered_iv, id.var="name")
# Plot a stacked barchart
ggplot(racecard_long_iv, aes(x = name, y = value, fill = variable)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

The highest bars indicate the highest cumulative Impact Values. Some bars do not include all four factors, as sometimes there were no results returned. For example, Bound For Nowhere’s sire, The Factor, has only had one Group runner in the UK & Ireland, which did not win. Therefore, there is no data to calculate strike rate, impact value etc.

This means one needs to be careful examining the chart and also take time to ponder the data in the dataframe. Some sample sizes may be very small and a question should be asked if they are statistically relevant. Bound For Nowhere’s Trainer & Jockey Impact Value is the highest in the race, but this is from only six runners. Compared to Merchant Navy with the second highest `tj_iv`

, but from 357 runners.

Nonetheless, a visual method like this can still assist to narrow the field. Harry Angel, the favourite for the race, is certainly not a standout in the chart, with decent sample sizes across all four factors.

The stacked barchart can also be applied to Actual vs Expected figures, strike rates or Confidence figures. The chart below displays stacked A/E for a more value oriented view.

```
# Filter for just the AE columns which we will plot
racecard_filtered_ae <- diamond_jubilee[,c("name","tr_ae","jc_ae", "tj_ae", "sr_ae")]
# Convert the racecard from wide to long format
racecard_long_ae <- melt(racecard_filtered_ae, id.var="name")
# Plot a stacked barchart
ggplot(racecard_long_ae, aes(x = name, y = value, fill = variable)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

Once again, keep Bound for Nowhere’s small sample sizes in mind. Harry Angel appears to be a better betting proposition based on this chart.

Another way to look at this data might be as a grouped bar chart, where the IV and A/E figures are plotted for each horse next to each other.

```
# Filter for just the AE columns which we will plot
racecard_filtered_all <- diamond_jubilee[,c("name","tr_iv","jc_iv", "tj_iv", "sr_iv",
"tr_ae","jc_ae", "tj_ae", "sr_ae")]
# Convert the racecard from wide to long format
racecard_long_all <- melt(racecard_filtered_all, id.var="name")
# Plot a grouped barchart
ggplot(racecard_long_all, aes(x = name, y = value, fill = variable)) +
geom_bar(position = "dodge", stat="identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

Although Sire IV is missing for Bound for Nowhere, his other figures do all point to a superier trainer and jockey, albeit from small sample size. How to statistically deal with these small sample sizes will be covered in a future article on Bayesian techniques.

Which horses might be included in a shortlist for today’s Diamond Jubilee? Even with small sample sizes, but knowing Wesley Ward’s Ascot success with sprinters, it may be wise to include Bound for Nowhere, who is currently 14.0 on Betfair. Merchant Navy (IV) and Harry Angel (A/E) are both positives, although much shorter priced at the top of the market.

It is important not to just rely on the data. There are many different factors to consider and a good knowledge of general form is also required. Therefore, after all that work, one might still decide just to back the Aussie danger and triple Group 1 sprint winner, Redkirk Warrior.

Good luck!

Questions and queries about this article should be posted as a comment below or on the Betwise Q&A board.

The full R code used in this article is found below.

```
# Load the library packages
library("RMySQL")
library("dplyr")
library("reshape2")
library("ggplot2")
# Connect to the Smartform database. Substitute the placeholder credentials for your own.
# The IP address can be substituted for a remote location if appropriate.
con <- dbConnect(MySQL(),
host='127.0.0.1',
user='yourusername',
password='yourpassword',
dbname='smartform')
# Select relevant historic results
sql1 <- paste("SELECT historic_races.course,
historic_races.meeting_date,
historic_races.conditions,
historic_races.group_race,
historic_races.race_type_id,
historic_races.race_type,
historic_races.distance_yards,
historic_runners.name,
historic_runners.jockey_name,
historic_runners.trainer_name,
historic_runners.finish_position,
historic_runners.starting_price_decimal,
historic_runners.trainer_id,
historic_runners.jockey_id,
historic_runners.sire_name
FROM smartform.historic_runners
JOIN smartform.historic_races USING (race_id)
WHERE historic_races.meeting_date >= '2012-01-01'", sep="")
smartform_results <- dbGetQuery(con, sql1)
# Remove non-runners and non-finishers
smartform_results <- dplyr::filter(smartform_results, !is.na(finish_position))
# Select relevant daily results for tomorrow
sql2 <- paste("SELECT daily_races.course,
daily_races.race_title,
daily_races.meeting_date,
daily_races.distance_yards,
daily_runners.cloth_number,
daily_runners.name,
daily_runners.trainer_name,
daily_runners.jockey_name,
daily_runners.sire_name,
daily_runners.forecast_price_decimal,
daily_runners.trainer_id,
daily_runners.jockey_id
FROM smartform.daily_races
JOIN smartform.daily_runners USING (race_id)
WHERE daily_races.meeting_date >='2018-06-23'", sep="")
smartform_daily_results <- dbGetQuery(con, sql2)
dbDisconnect(con)
# Remove non-runners
smartform_daily_results <- dplyr::filter(smartform_daily_results, !is.na(forecast_price_decimal))
# Trainer stats
# Name the function and add some arguments
tr <- function(race_filter = "", price_filter = 1000, trainer){
# Filter for flat races only
flat_races_only <- dplyr::filter(smartform_results,
race_type_id == 12 |
race_type_id == 15)
# Add an if else statement for the race_filter argument
if (race_filter == "group"){
filtered_races <- dplyr::filter(flat_races_only,
group_race == 1 |
group_race == 2 |
group_race == 3 )
} else {
filtered_races = flat_races_only
}
# Filter by trainer name
trainer_filtered <- dplyr::filter(filtered_races,
grepl(trainer, trainer_id))
# Filter by price
trainer_price_filtered <- dplyr::filter(trainer_filtered,
starting_price_decimal <= price_filter)
# Calculate Profit and Loss
trainer_cumulative <- cumsum(
ifelse(trainer_price_filtered$finish_position == 1,
(trainer_price_filtered$starting_price_decimal-1),
-1)
)
# Calculate Strike Rate
winners <- nrow(dplyr::filter(trainer_price_filtered,
finish_position == 1))
runners <- nrow(trainer_price_filtered)
strike_rate <- (winners / runners) * 100
# Calculate Profit on Turnover or Yield
profit_on_turnover <- (tail(trainer_cumulative, n=1) / runners) * 100
# Check if POT is zero length to catch later errors
if (length(profit_on_turnover) == 0) profit_on_turnover <- 0
# Calculate Impact Values
# First filter all runners by price, to return those just starting at the price_filter or less
all_runners <- nrow(dplyr::filter(filtered_races,
starting_price_decimal <= price_filter))
# Filter all winners by the price filter
all_winners <- nrow(dplyr::filter(filtered_races,
finish_position == 1 &
starting_price_decimal <= price_filter))
# Now calculate the Impact Value
iv <- (winners / all_winners) / (runners / all_runners)
# Calculate Actual vs Expected ratio
# # Convert all decimal odds to probabilities
total_sp <- sum(1/trainer_price_filtered$starting_price_decimal)
# Calculate A/E by dividing the number of winners, by the sum of all SP probabilities.
ae <- winners / total_sp
# Calculate Archie
archie <- (runners * (winners - total_sp)^2)/ (total_sp * (runners - total_sp))
# Calculate the Confidence figure
conf <- pchisq(archie, df = 1)*100
# Create an empty variable
trainer <- NULL
# Add all calculated figures as named objects to the variable, which creates a list
trainer$tr_runners <- runners
trainer$tr_winners <- winners
trainer$tr_sr <- strike_rate
trainer$tr_pot <- profit_on_turnover
trainer$tr_iv <- iv
trainer$tr_ae <- ae
trainer$tr_conf <- conf
# Add an error check to convert all NaN values to zero
final_results <- unlist(trainer)
final_results[ is.nan(final_results) ] <- 0
# Manipulate the layout of returned results to be a nice dataframe
final_results <- t(as.data.frame(final_results))
rownames(final_results) <- c()
# 2 decimal places only
round(final_results, 2)
# Finally, close the function
}
# Jockey stats
# Name the function and add some arguments
jc <- function(race_filter = "", price_filter = 1000, jockey){
# Filter for flat races only
flat_races_only <- dplyr::filter(smartform_results,
race_type_id == 12 |
race_type_id == 15)
# Add an if else statement for the race_filter argument
if (race_filter == "group"){
filtered_races <- dplyr::filter(flat_races_only,
group_race == 1 |
group_race == 2 |
group_race == 3 )
} else {
filtered_races = flat_races_only
}
# Filter by trainer name
jockey_filtered <- dplyr::filter(filtered_races,
grepl(jockey, jockey_id))
# Filter by price
jockey_price_filtered <- dplyr::filter(jockey_filtered,
starting_price_decimal <= price_filter)
# Calculate Profit and Loss
jockey_cumulative <- cumsum(
ifelse(jockey_price_filtered$finish_position == 1,
(jockey_price_filtered$starting_price_decimal-1),
-1)
)
# Calculate Strike Rate
winners <- nrow(dplyr::filter(jockey_price_filtered,
finish_position == 1))
runners <- nrow(jockey_price_filtered)
strike_rate <- (winners / runners) * 100
# Calculate Profit on Turnover or Yield
profit_on_turnover <- (tail(jockey_cumulative, n=1) / runners) * 100
# Check if POT is zero length to catch later errors
if (length(profit_on_turnover) == 0) profit_on_turnover <- 0
# Calculate Impact Values
# First filter all runners by price, to return those just starting at the price_filter or less
all_runners <- nrow(dplyr::filter(filtered_races,
starting_price_decimal <= price_filter))
# Filter all winners by the price filter
all_winners <- nrow(dplyr::filter(filtered_races,
finish_position == 1 &
starting_price_decimal <= price_filter))
# Now calculate the Impact Value
iv <- (winners / all_winners) / (runners / all_runners)
# Calculate Actual vs Expected ratio
# # Convert all decimal odds to probabilities
total_sp <- sum(1/jockey_price_filtered$starting_price_decimal)
# Calculate A/E by dividing the number of winners, by the sum of all SP probabilities.
ae <- winners / total_sp
# Calculate Archie
archie <- (runners * (winners - total_sp)^2)/ (total_sp * (runners - total_sp))
# Calculate the Confidence figure
conf <- pchisq(archie, df = 1)*100
# Create an empty variable
jockey <- NULL
# Add all calculated figures as named objects to the variable, which creates a list
jockey$jc_runners <- runners
jockey$jc_winners <- winners
jockey$jc_sr <- strike_rate
jockey$jc_pot <- profit_on_turnover
jockey$jc_iv <- iv
jockey$jc_ae <- ae
jockey$jc_conf <- conf
# Add an error check to convert all NaN values to zero
final_results <- unlist(jockey)
final_results[ is.nan(final_results) ] <- 0
# Manipulate the layout of returned results to be a nice dataframe
final_results <- t(as.data.frame(final_results))
rownames(final_results) <- c()
# 2 decimal places only
round(final_results, 2)
# Finally, close the function
}
# Trainer and Jockey stats
# Name the function and add some arguments
tj <- function(race_filter = "", price_filter = 1000, trainer, jockey){
# Filter for flat races only
flat_races_only <- dplyr::filter(smartform_results,
race_type_id == 12 |
race_type_id == 15)
# Add an if else statement for the race_filter argument
if (race_filter == "group"){
filtered_races <- dplyr::filter(flat_races_only,
group_race == 1 |
group_race == 2 |
group_race == 3 )
} else {
filtered_races = flat_races_only
}
# Filter by trainer name
trainer_filtered <- dplyr::filter(filtered_races,
grepl(trainer, trainer_id))
# Remove non-runners
# trainer_name_filtered <- dplyr::filter(trainer_filtered, !is.na(finish_position))
# Filter by jockey name
trainer_jockey_filtered <- dplyr::filter(trainer_filtered,
grepl(jockey, jockey_id))
# Filter by price
trainer_jockey_price_filtered <- dplyr::filter(trainer_jockey_filtered,
starting_price_decimal <= price_filter)
# Calculate Profit and Loss
trainer_jockey_cumulative <- cumsum(
ifelse(trainer_jockey_price_filtered$finish_position == 1,
(trainer_jockey_price_filtered$starting_price_decimal-1),
-1)
)
# Calculate Strike Rate
winners <- nrow(dplyr::filter(trainer_jockey_price_filtered,
finish_position == 1))
runners <- nrow(trainer_jockey_price_filtered)
strike_rate <- (winners / runners) * 100
# Calculate Profit on Turnover or Yield
profit_on_turnover <- (tail(trainer_jockey_cumulative, n=1) / runners) * 100
# Check if POT is zero length to catch later errors
if (length(profit_on_turnover) == 0) profit_on_turnover <- 0
# Calculate Impact Values
# First filter all runners by price, to return those just starting at the price_filter or less
all_runners <- nrow(dplyr::filter(filtered_races,
starting_price_decimal <= price_filter))
# Filter all winners by the price filter
all_winners <- nrow(dplyr::filter(filtered_races,
finish_position == 1 &
starting_price_decimal <= price_filter))
# Now calculate the Impact Value
iv <- (winners / all_winners) / (runners / all_runners)
# Calculate Actual vs Expected ratio
# # Convert all decimal odds to probabilities
total_sp <- sum(1/trainer_jockey_price_filtered$starting_price_decimal)
# Calculate A/E by dividing the number of winners, by the sum of all SP probabilities.
ae <- winners / total_sp
# Calculate Archie
archie <- (runners * (winners - total_sp)^2)/ (total_sp * (runners - total_sp))
# Calculate the Confidence figure
conf <- pchisq(archie, df = 1)*100
# Create an empty variable
trainer_jockey <- NULL
# Add all calculated figures as named objects to the variable, which creates a list
trainer_jockey$tj_runners <- runners
trainer_jockey$tj_winners <- winners
trainer_jockey$tj_sr <- strike_rate
trainer_jockey$tj_pot <- profit_on_turnover
trainer_jockey$tj_iv <- iv
trainer_jockey$tj_ae <- ae
trainer_jockey$tj_conf <- conf
# Add an error check to convert all NaN values to zero
final_results <- unlist(trainer_jockey)
final_results[ is.nan(final_results) ] <- 0
# Manipulate the layout of returned results to be a nice dataframe
final_results <- t(as.data.frame(final_results))
rownames(final_results) <- c()
# 2 decimal places only
round(final_results, 2)
# Finally, close the function
}
# Sire stats
# Name the function and add some arguments
sr <- function(race_filter = "", price_filter = 1000, sire){
# Filter for flat races only
flat_races_only <- dplyr::filter(smartform_results,
race_type_id == 12 |
race_type_id == 15)
# Add an if else statement for the race_filter argument
if (race_filter == "group"){
filtered_races <- dplyr::filter(flat_races_only,
group_race == 1 |
group_race == 2 |
group_race == 3 )
} else {
filtered_races = flat_races_only
}
# Filter by trainer name
sire_filtered <- dplyr::filter(filtered_races,
grepl(sire, sire_name))
# Filter by price
sire_price_filtered <- dplyr::filter(sire_filtered,
starting_price_decimal <= price_filter)
# Calculate Profit and Loss
sire_cumulative <- cumsum(
ifelse(sire_price_filtered$finish_position == 1,
(sire_price_filtered$starting_price_decimal-1),
-1)
)
# Calculate Strike Rate
winners <- nrow(dplyr::filter(sire_price_filtered,
finish_position == 1))
runners <- nrow(sire_price_filtered)
strike_rate <- (winners / runners) * 100
# Calculate Profit on Turnover or Yield
profit_on_turnover <- (tail(sire_cumulative, n=1) / runners) * 100
# Check if POT is zero length to catch later errors
if (length(profit_on_turnover) == 0) profit_on_turnover <- 0
# Calculate Impact Values
# First filter all runners by price, to return those just starting at the price_filter or less
all_runners <- nrow(dplyr::filter(filtered_races,
starting_price_decimal <= price_filter))
# Filter all winners by the price filter
all_winners <- nrow(dplyr::filter(filtered_races,
finish_position == 1 &
starting_price_decimal <= price_filter))
# Now calculate the Impact Value
iv <- (winners / all_winners) / (runners / all_runners)
# Calculate Actual vs Expected ratio
# # Convert all decimal odds to probabilities
total_sp <- sum(1/sire_price_filtered$starting_price_decimal)
# Calculate A/E by dividing the number of winners, by the sum of all SP probabilities.
ae <- winners / total_sp
# Calculate Archie
archie <- (runners * (winners - total_sp)^2)/ (total_sp * (runners - total_sp))
# Calculate the Confidence figure
conf <- pchisq(archie, df = 1)*100
# Create an empty variable
sire <- NULL
# Add all calculated figures as named objects to the variable, which creates a list
sire$sr_runners <- runners
sire$sr_winners <- winners
sire$sr_sr <- strike_rate
sire$sr_pot <- profit_on_turnover
sire$sr_iv <- iv
sire$sr_ae <- ae
sire$sr_conf <- conf
# Add an error check to convert all NaN values to zero
final_results <- unlist(sire)
final_results[ is.nan(final_results) ] <- 0
# Manipulate the layout of returned results to be a nice dataframe
final_results <- t(as.data.frame(final_results))
rownames(final_results) <- c()
# 2 decimal places only
round(final_results, 2)
# Finally, close the function
}
# Filter tomorrow's races for Group races only
group_races_only <- dplyr::filter(smartform_daily_results,
grepl(paste(c("Group 1", "Group 2", "Group 3"), collapse="|"), race_title))
# Create placeholder lists which will be required later
row_tr <- list()
row_jc <- list()
row_tj <- list()
row_sr <- list()
# Setup the loop
# For each horse in the group_races_only dataframe
for (i in group_races_only$name) {
runner_details = group_races_only[group_races_only$name==i,]
# Extract trainer and jockey names
trainer <- runner_details$trainer_id
jockey <- runner_details$jockey_id
sire <- runner_details$sire_name
# Apply the Trainer function for Group races only
trainer_combo <- tr(race_filter = "group",
trainer = trainer)
# Add results row by row to the previously defined list
row_tr[[i]] <- trainer_combo
# Apply the Jockey function for Group races only
jockey_combo <- jc(race_filter = "group",
jockey = jockey)
# Add results row by row to the previously defined list
row_jc[[i]] <- jockey_combo
# Apply the Trainer/Jockey function for Group races only
trainer_jockey_combo <- tj(race_filter = "group",
trainer = trainer, jockey = jockey)
# Add results row by row to the previously defined list
row_tj[[i]] <- trainer_jockey_combo
# Apply the Sire function for Group races only
sire_combo <- sr(race_filter = "group",
sire = sire)
# Add results row by row to the previously defined list
row_sr[[i]] <- sire_combo
# Create a final dataframe
stats_final_tr <- as.data.frame(do.call("rbind", row_tr))
stats_final_jc <- as.data.frame(do.call("rbind", row_jc))
stats_final_tj <- as.data.frame(do.call("rbind", row_tj))
stats_final_sr <- as.data.frame(do.call("rbind", row_sr))
}
# Create a new variable called racecard. Bind together the generic race details with the newly created stats
racecard <- cbind(group_races_only,stats_final_tr)
racecard <- cbind(racecard,stats_final_jc)
racecard <- cbind(racecard,stats_final_tj)
racecard <- cbind(racecard,stats_final_sr)
# Filter for Diamond Jubilee Only
diamond_jubilee <- dplyr::filter(racecard,
grepl("Diamond Jubilee",
race_title))
# Filter for just the IV columns which we will plot
racecard_filtered_iv <- diamond_jubilee[,c("name","tr_iv","jc_iv", "tj_iv", "sr_iv")]
# Convert the racecard from wide to long format
racecard_long_iv <- melt(racecard_filtered_iv, id.var="name")
# Plot a stacked barchart
ggplot(racecard_long_iv, aes(x = name, y = value, fill = variable)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Filter for just the AE columns which we will plot
racecard_filtered_ae <- diamond_jubilee[,c("name","tr_ae","jc_ae", "tj_ae", "sr_ae")]
# Convert the racecard from wide to long format
racecard_long_ae <- melt(racecard_filtered_ae, id.var="name")
# Plot a stacked barchart
ggplot(racecard_long_ae, aes(x = name, y = value, fill = variable)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Filter for just the AE columns which we will plot
racecard_filtered_all <- diamond_jubilee[,c("name","tr_iv","jc_iv", "tj_iv", "sr_iv",
"tr_ae","jc_ae", "tj_ae", "sr_ae")]
# Convert the racecard from wide to long format
racecard_long_all <- melt(racecard_filtered_all, id.var="name")
# Plot a grouped barchart
ggplot(racecard_long_all, aes(x = name, y = value, fill = variable)) +
geom_bar(position = "dodge", stat="identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```