Evening all,
I was looking at one of Peter's videos recently (https://www.youtube.com/watch?v=z5fyLqVCqjE) on him speaking about how often the favourite actually went on to win.
At around 6mins30 he explains how one of the lines on his graph was created as a measure of market variance. The grey line seen, he explains, came from the variation in odds available, presumably across the favourites from his sample data of around 900 races, compared to how many of those then went on to win.
I've been thinking about it for a while and just cannot get my head around how one would make a comparison of that variation and turn that value into a % to ensure market efficiency and balance.
This was more to see if there was someone who may be able to explain it, it's going round and round in my head!
Any thoughts welcome.
How often the Fav. wins
Warren, this is my interpretation of what Peter looks to show in the video. Assuming you already have some data, make sure you have a column containing 1`s against every record as it`s this that will drive your results. Mine is called Counter.
This is taken from BF Promo data for Aus races and is filtered for Rank 1. The Sum of Counter counts the Win/Loss per month and the Pivot sorts by % of Col`n Total. Very handy as it gives the values we are looking for.
Below the Pivot is the data that drives the charts.
Win % comes from the 1 Row above (the % of wins that month)
Average Win % is the Average of the same row over the same time period
Variance is the Average subtracted from the Win%
Level is a baseline of 0%
The chart on the right shows 2 views. Win % v Average Win % and Variance v Level. Both are essentially showing the same thing
The chart on the left uses the same data to plot how the win% is changing over time v the average and with a Linear which looks to indicate the win rate increasing.
Can elaborate but obviously not sure of your Pivot/Charting skills so feel free to ask if it`s unclear
This is taken from BF Promo data for Aus races and is filtered for Rank 1. The Sum of Counter counts the Win/Loss per month and the Pivot sorts by % of Col`n Total. Very handy as it gives the values we are looking for.
Below the Pivot is the data that drives the charts.
Win % comes from the 1 Row above (the % of wins that month)
Average Win % is the Average of the same row over the same time period
Variance is the Average subtracted from the Win%
Level is a baseline of 0%
The chart on the right shows 2 views. Win % v Average Win % and Variance v Level. Both are essentially showing the same thing
The chart on the left uses the same data to plot how the win% is changing over time v the average and with a Linear which looks to indicate the win rate increasing.
Can elaborate but obviously not sure of your Pivot/Charting skills so feel free to ask if it`s unclear
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Amazing thank you for showing an example of how one would break it down. Use of pivot tables certainly makes for more readily consumable data and the graphs again make that process sleeker.
Could you see a way in which you could include the BSP on those favourites as to how that then affected their perceived performance vs their actual?
W
Could you see a way in which you could include the BSP on those favourites as to how that then affected their perceived performance vs their actual?
W
I group the BSP`s using =FLOOR(cell,0.1) to give a data set that has increments the same size so 2 odds takes in >=2 and <2.1 just so you know.
Including BSP Floor Fine as it`s called as part of the Pivot criteria it`s easy to find the odd you are looking for in the dropdown menu. This is Rank 1 and 2 odds.
So it looks to be underperforming when compared to it`s implied probability of 50% returning an average of 47.91% over the data set. It has both overperforming/underperforming days,weeks,months as the monthly chart and data shows. The £ returns look to reflect this.
By comparison this is still Rank1 but 3.5 odds. Over the same period this is overperforming. Implied probability is 28.57% but is averaging 30.08% and again the £ returns look to reflect this.
Including BSP Floor Fine as it`s called as part of the Pivot criteria it`s easy to find the odd you are looking for in the dropdown menu. This is Rank 1 and 2 odds.
So it looks to be underperforming when compared to it`s implied probability of 50% returning an average of 47.91% over the data set. It has both overperforming/underperforming days,weeks,months as the monthly chart and data shows. The £ returns look to reflect this.
By comparison this is still Rank1 but 3.5 odds. Over the same period this is overperforming. Implied probability is 28.57% but is averaging 30.08% and again the £ returns look to reflect this.
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Really interesting stuff with the pivot tables here, gives me thoughts to how I'm displaying my own data.
Where you're saying BSP floor at, for example 2, is that saying taking all Rank 1 runners with odds below/above 2 or just around that value?
Likewise you then can alter that floor to say 4 and it does the same, expanding/decreasing the number of runners selected by the floor of 4 criteria?
W
Where you're saying BSP floor at, for example 2, is that saying taking all Rank 1 runners with odds below/above 2 or just around that value?
Likewise you then can alter that floor to say 4 and it does the same, expanding/decreasing the number of runners selected by the floor of 4 criteria?
W
REALLY NICE WORK Atho55! Very impressed!Atho55 wrote: ↑Fri Apr 17, 2020 4:20 pmI group the BSP`s using =FLOOR(cell,0.1) to give a data set that has increments the same size so 2 odds takes in >=2 and <2.1 just so you know.
Including BSP Floor Fine as it`s called as part of the Pivot criteria it`s easy to find the odd you are looking for in the dropdown menu. This is Rank 1 and 2 odds.
Aus Rank 1 2 odds.jpg
So it looks to be underperforming when compared to it`s implied probability of 50% returning an average of 47.91% over the data set. It has both overperforming/underperforming days,weeks,months as the monthly chart and data shows. The £ returns look to reflect this.
By comparison this is still Rank1 but 3.5 odds. Over the same period this is overperforming. Implied probability is 28.57% but is averaging 30.08% and again the £ returns look to reflect this.
Aus Rank 1 3.5 odds.jpg
How have you excluded/included Dead Heats into this data?
Also have you got a copy you could post please?
Regards
G
I had similar thoughts, and also at 6:34 mins Peter said "How many horses actual won in that particular race" which only makes sense if it was a Dead Heat. Can anyone explain this? I would also like to see a video on how he deals with the data and sorts it into the spreadsheet.warren0 wrote: ↑Wed Apr 15, 2020 9:47 pmEvening all,
I was looking at one of Peter's videos recently (https://www.youtube.com/watch?v=z5fyLqVCqjE) on him speaking about how often the favourite actually went on to win.
At around 6mins30 he explains how one of the lines on his graph was created as a measure of market variance. The grey line seen, he explains, came from the variation in odds available, presumably across the favourites from his sample data of around 900 races, compared to how many of those then went on to win.
I've been thinking about it for a while and just cannot get my head around how one would make a comparison of that variation and turn that value into a % to ensure market efficiency and balance.
This was more to see if there was someone who may be able to explain it, it's going round and round in my head!
Any thoughts welcome.
Can someone double check this for me please? I am trying to work out the Average BSP but if i add all the BSPs together then take off the number of runners then divide by the number of runners then add the 1 back on i get a different number when I do the the calculation 1/BSP = % implied odds then add them all up and divide by the number of runners.
BSP 4.21
BSP 2.38
1. (4.21+2.38) = 6.59 , 6.59 - 2 = 4.59, 4.59/2 = 2.295, 2.295 + 1 = Average BSP 3.30
2. Implied Odds % so (1/4.21)+(1/2.38) = 0.2375 + 0.420 = 0.6577/2 = 0.329, 1/0.329 = Average BSP 3.04
Which is correct?
BSP 4.21
BSP 2.38
1. (4.21+2.38) = 6.59 , 6.59 - 2 = 4.59, 4.59/2 = 2.295, 2.295 + 1 = Average BSP 3.30
2. Implied Odds % so (1/4.21)+(1/2.38) = 0.2375 + 0.420 = 0.6577/2 = 0.329, 1/0.329 = Average BSP 3.04
Which is correct?