So watching footyclassified and Matthew Lloyd made a comment that it would seem as though the key to beating Richmond is controlling the ball via having more kicks to handballs.
Got me thinking, wouldn’t that be cool if a plot showed the same insight that an industry professional had?
Well lets do that plot!
## -- Attaching packages ------------------
## v ggplot2 126.96.36.19900 v purrr 0.2.5 ## v tibble 1.4.2 v dplyr 0.7.6 ## v tidyr 0.8.1 v stringr 1.3.1 ## v readr 1.1.1 v forcats 0.3.0
## -- Conflicts -- tidyverse_conflicts() -- ## x dplyr::filter() masks stats::filter() ## x dplyr::lag() masks stats::lag()
## Getting data from footywire.com
## Finished getting data
df%>% select(Round, Team, Opposition, K, HB)%>% filter(Opposition=="Richmond")%>% group_by(Round, Team, Opposition)%>% summarise(kicks=sum(K), handballs=sum(HB))%>% ggplot(aes(x=kicks, y=handballs, label=Round))+geom_point()+ geom_text(position=position_jitter(height=3)) + geom_curve(aes(x = 220, y = 120, xend = 260, yend = 200, colour = "curve"), curvature = -.35)+ theme(legend.position="none") + ggtitle("Richmond Oppositions Kicks and Handball Totals")+ ylab("Richmond Handballs Conceded") + xlab("Richmond Kicks conceded")
The interesting thing about this plot is that if you look at the red line, all games to the right of it Richmond have lost?
Was Lloydy onto something?
Hopefully now you have a template and hopefully you are a little bit keener on using it to derive your own non Lloydy insights. Give it a go, its suprisingly addictive….
As always, got any questions, hit me up on twitter using the #makemeauseR or feel free to email.