Middlesbrough started the season as the undisputed favourites to win the Sky bet Championship and gain promotion back to the Premier League. This came from the chairman’s publicly stated ambition to “smash the league” and a total transfer spend of over £40 million (although net transfer spend was only around £15 million). There was high hopes across Teesside that the newly put together team would be able to do just that. However things haven’t turned out anything like that for the Teessiders. In the wake of Saturday nights chastening defeat to Bristol City the team are now 14 points behind Cardiff City and the second automatic promotion place.
I want to try and cut through the opinion and look at one graph which probably wont give a definitive answer on the question above however will inform some views on how Steve Gibson should be thinking.
The graph above plots the points per game. For reference I have added the points per game on average required to avoid relegation, reach the playoff and achieve automatic promotion. On that I have plotted the actual points per game and the expected points per game, which is the critical piece of information. The expected points if derived from expected goals for each match and I have included it as that value will give an understanding of the luck. If a team was outperforming expected points then they could be termed as lucky and vice versa. Reviewing the trend for Middlesbrough this has unfortunately not being the case. Their actual points is within 2 points of the expected points therefore i think they are exactly were there performances deserve to be. This is bad news for Gary Monk as this performance is far below what i expect the minimum expectation was for the season, automatic promotion. I wouldn’t say this definitely means he should sacked however this is not good statistics. I will do further posts over the week to dissect if there are any clear reasons why.
In this post I wanted to have a look at how much goalkeepers are used in football. There’s a lot of talk about how goalkeepers are used in modern football particularly due to Pep Guardiola wanting a goalkeeper that can help play out the back. In order to do this i looked at the Championship in England. Mainly because my team is in that league.
I took each teams total touches per match and the number of goalkeeper touches. I then calculated the percentage of touches for the goal keeper. The results for the Championship are in the table. What is immediately apparent to me that goalkeeper usage is probably not just tactical way of playing but also influenced by how good the team as a whole is. Teams not comfortable on the ball are more likely to use the goalkeeper to knock the ball long. Wolves have signed players all comfortable on the ball which has meant the goalkeeper has been used less. Also goalkeeper usage is influenced by the number of goal kicks. this could be removed from future analysis as this could effect the results that are seen.
The graph below shows how the number of points a team accumulated over the first 11 games is related to the % of touches the goal keeper had. There seems to be a fairly weak correlation with the number of points a team has however it will be interesting to see how this develops as the season goes on.
I am going to further review other positions touches for the teams in the championship. Possibly look at centre backs and where they touch it and full backs.
In order to further review the performances of the bowlers in the 2017 IPL i have came up with a metric that takes various information such as number of wickets, strike rate, noumber of boundries conceded, dot balls. All things that are crucial to reviewing a bowler perfromance in the IPL.
The graph shows the top 43 bowlers in the 2017 IPL and there performance rating based on the factors already mentioned. Pawan Negi based on this metric was the best performing bowler which is surprising as 10 players took more wickets then him. However he was economical and bowled significantly less balls then the bowlers who took more wickets then him. This is why a metric like this is helpful as it helps you look past the wickets taken. At the other end of the scale is James Falkner who seems to have performed significantly worse then the next bowler. This seems to be down to low amount of wickets taken and high economy rate. Now this could be because of when he was bowling for instance if hes bowling in the powerplay for most of his overs.
The plot above is the same plot however i have coloured the plot in order to show which team the bowler belongs too. There seems to be no big trend however the winners of the competition Mumbai have the most bowlers in the top 15 with 4.
The graph above i have now coloured the columns by the type of bowler. Nothing particularly stands out here. Overall this is a first attempt at reviewing the performance ratios. What would further develop this is maybe a look at this against there value in the auction. I think this can also be developed game by game and for batsmen as well.
The IPL finished at the end of May with the Mumbai Indians winning the title on the last ball of the match against the now defunct Rising Pune Super Giant. I decided to take a new look at evaluating bowling performances. Currently bowlers in all cricket are judged based on the amount of wickets they have taken, their economy rate or their bowling average. I think there could be a new way of reviewing performances. If you take the batsmen average as an indication of how good the batsmen is the batsmen with higher averages must be harder wickets to take. Therefore if you record the average of the batsmen the bowler took the wicket of you can make a judgement of the quality of the bowling. Has the bowler took a lot of wickets but where they all lower order less quality batsmen or are they good at taking the top order wickets.
I have used this approach to review the bowling performances in the 2017 IPL. To qualify for this review the bowler had to have taken more then 5 wickets which left me with 44 bowlers. The first graph below shows the amount of wickets taken by bowler against the number of balls they bowled:
Stand out performances from that graph are clearly Unadkat and Bhuvneshwar Kumar taking lots more wickets then anyone else who bowled a similar amount of balls. Sunil Narine looks to have had a significantly lower return of wickets then he should have done with the amount of balls bowled. Now if we look at the quality of the batsmen the bowlers got out shown in the graph below:
As you can see our second ranked bowler based on wickets taken has one of the lowest average batting quality rating (have to come up with a better name!) at 18.45, suggesting that he often got his wickets from lower order batsmen. Kumar got his wickets at an Batsmen average of 22.33 which is a respectable value slightly lower then the average for all bowlers. The two highest bowlers where Chawler and Nadeem both only took 6 wickets but didn’t bowl many balls (only 120 and 107 respectively). Would be interesting to know why they played only 6 games as they both looked to have troubled a lot of higher ranking batsmen.
That’s it for this first look as a way to review bowling performances. I think this can be explored a lot more and will definitely look to do that. The weakness of the method is the amount of games a batsmen has played as one that has played few will possibly have an unrepresentative average.