Females v Males. Is There A Bias Against Either Sex On The Racetrack?

Updated: 6368 Horse Racing

Are female racehorses at a disadvantage when racing againstmales?I did some research on this subject following a discussion onthe horse racing forum, and I was surprised to find that althoughthe gender bias is widely acknowledged by the racing fraternity, itdoesn't

Females v Males. Is There A Bias Against Either Sex On The Racetrack?
Andy Powell Content Editor

Horse Racing stats man, Andy has contributed to OLBG for 18 years - An Ipswich fan and F1 fanatic, he also contributes EFL football and Motor Sport opinion.

Are female racehorses at a disadvantage when racing against males?

I did some research on this subject following a discussion on the horse racing forum and I was surprised to find that although the gender bias is widely acknowledged by the racing fraternity, it doesn't appear to have as much of an impact on the way punters bet.

Or at least, a check of past results showed that the betting markets might not be giving this bias enough weighting. 

Perhaps headline mares such as Zenyatta, Black Caviar, Moonlight Cloud, Treve and NH stars like Annie Power and Quevega have the effect of diminishing the view amongst punters that female racehorses are disadvantaged against males.

I'm sure there has been plenty of research done by systems bettors and others looking for a winner finding edge though. 

There was an article published on Flatstats.com in 2002 that showed a definite bias in favour of males and other Internet betting forums have touched on the subject.

The perception is that female racehorse are particularly disadvantaged on the All-Weather surfaces, and this is where my research took me after seeing the breakdown of the figures in Table1.


Cheltenham, OLBG Mares Hurdle 2013



A note on females in NH racing

There are also lower than average figures for females in the NH sphere. The bias against fillies and mares when competing against males over obstacles has long been known. 

The weight allowance for fillies and mares was increased from 5lbs to 7lbs in 2003 (and in Ireland in 2011) in an attempt to allow mares to become more competitive, and for a time this made for an increase in the number of mares that stayed in training. 

The increase in the weight allowance has not fully solved the issue, however, and the authorities in the UK and in Ireland have steadily increased the amount of races restricted to Fillies and Mares. 

In 2013 the number of races restricted to Fillies and Mares was increased by nearly 20% (from 319 to 380), and much is continually being done to increase incentives for Trainers, Owners and Breeders to race more mares in the NH sphere.

It may be worthwhile breaking down the NH figures in Table 1 to see if there is any aspect of NH racing that is particularly disadvantageous to females, however, I haven't done any further research in this area myself.



I've presented the results of the research in tables. 

The first part is a general look at all mixed-sex races.

Table 1 firstly has all races combined (NH Rules, Turf, and All-Weather), and then these figures are broken down into the 3 race types. 

It was at this point that I decided to limit the research to All-Weather racing for this blog.

Part 1.

The starting point was to look at all races which had the exact same number (50%) of Female and Male runners. 

The first figures in Table 1 are for every race combined, with no filters for Race Type, Age Groups, Distances, etc. 

Therefore the first set of figures will include all Mixed Sex 2yo Nurseries with 6 runners for example, alongside all 24 runner Handicap Chases, and every race in between. 

The only criteria for this first set of figures was that every race consisted of both Male and Female runners.

The Table then goes on to break down the 50:50 races into each Type of race (National Hunt, Turf, and All Weather).

To support the figures from the 50:50 races, the Table then continues with races that have a different ratio of Males to Females. I have looked at:

  • All races that have from 1-49% males.
  • All races that have from 51-99% males.
  • All races that have from 33-67% males.

Again, each set of figures is then broken down into the 3 Race Types.

Table 1 probably covers more than it needs to. Each set of figures from the different ratios of Males:Females supports the figures from the first part (the 50:50 races) of the Table. 

The sets of figures for even more exact percentages of Males (exactly 33%, exactly 40% etc) would have smaller than ideal sample sizes, and showing them would be an overload of data, if it isn't already.

Table # 1 is only a very general overview. 

The figures suggest a marked difference in the performance of Female horses on the All Weather surfaces (and also in the National Hunt sphere), compared to the Turf, and I've mainly shown these figures to justify the focussing of my research on the All-Weather surfaces. 

There could well be certain categories of Turf racing that favours one sex over the other, but as with NH racing any research on the Turf will be done at another time.

The second part of Table 1 looks at the performance of the (outright) SP favourite according to the sex of the horse, from all mixed sex races that consisted of 25-75% Males (so in other words, no race had less than 25% representation of either sex),

The assumption was that a 2.00 favourite had a 50% chance of winning the race (disregarding the market overround) regardless of the sex of the horse. 

Plenty of research has been done to show that the finishing position of a horse correlates with its position in the betting market, and any bias should show up when looking at how the favourite performed, and whether or not the market has made adjustments to the price of any runner based on any perceived advantage to either sex. 

For this part of the table I've included Backing and Laying calculations, with Profit/Loss figures and ROI percentages at Industry SP, and Laying calculations at Betfair SP. 

These calculations didn't seem of any real benefit for the first section of the table, and weren't included there.

Race Type Sex Runners % of Runners Races Wins % Of total wins A:E figure
Race Type Sex Runners % of Runners Races Wins % Of total wins A:E figure
Race Type Sex Runners % of Runners Races Wins % Of total wins A:E figure
Race Type Sex Runners % of Runners Races Wins % Of total wins A:E figure
Table 1.
Races With Exactly 50% Males/Females
All Race Types. Males 21108 50% 4374 2561 58.55% 0.88
Females 21108 50% 4374 1821 41.63% 0.78
NH Races Only Males 3532 50% 647 418 64.61% 0.88
Females 3532 50% 647 230 35.55% 0.66
Turf Races Only Males 11139 50% 2341 1271 54.29% 0.85
Females 11139 50% 2341 1075 45.92% 0.84
AW Races Only Males 6437 50% 1386 872 62.69% 0.93
Females 6437 50% 1386 516 37.23% 0.74
The three parts of this table that follow probably don't offer anything of much value. They are included to give support to the first part of the table, and to give a general look at female performances from larger sample sizes. More useful figures are in the later tables that cover Age Groups, Distances, Tracks and Surfaces etc.
Races With 1-49% Males.
All Race Types Males 36716 37.32% 9176 4050 44.03% 0.86
Females 61665 62.68% 9176 5151 56.14% 0.78
NH Races Only Males 3886 40.59% 786 452 57.50% 0.88
Females 5688 59.41% 786 334 42.50% 0.63
Turf Races Only Males 22805 36.95% 5615 2294 40.85% 0.83
Females 38920 63.05% 5615 3331 59.32% 0.80
AW Races Only Males 10025 37.02% 2775 1294 46.63% 0.93
Females 17057 62.98% 2775 1486 53.55% 0.79

Races With 51-99% Males.
All Race Types Males 690674 77.00% 78607 64655 82.25% 0.83
Females 206784 23.00% 78607 14105 17.94% 0.76
NH Races Only Males 312852 79.80% 32492 27963 86.06% 0.82
Females 79674 20.30% 32492 4568 14.06% 0.71
Turf Races Only Males 254353 75.20% 30077 23651 78.63% 0.84
Females 84043 24.80% 30077 6496 21.60% 0.81
AW Races Only Males 123479 74.10% 16038 13041 81.31% 0.87
Females 43067 25.90% 16038 3041 18.96% 0.74

Races With 33-67% Males.
All Race Types Males 190820 55.30% 31461 19918 63.31% 0.85
Females 154356 44.70% 31461 11604 36.88% 0.76
NH Races Only Males 47538 58.58% 6624 4749 71.69% 0.83
Females 33652 41.44% 6624 1884 28.44% 0.65
Turf Races Only Males 94335 54.03% 16005 9406 58.77% 0.83
Females 80253 45.97% 16005 6632 41.44% 0.81
AW Races Only Males 48947 54.75% 8832 5763 65.25% 0.90
Females 40451 45.25% 8832 3088 34.96% 0.75
Performance of SP Favourite as Male/Female In Mixed Sex Races With No Less Than 25% Representation Of Either Sex.
Race Type Sex Runners Wins Win % PL @ SP ROI P/L Lay ROI Lay A:E Figure
NH, Turf, AW combined. Males 31,257 10,912 34.91% -1,687 -5.04% -558.32 -1.35% 0.95
Females 12,543 3,835 30.57% -1,433 -11.43% -32.45 -0.17 0.89
NH Rules Males 9,071 3,363 37.07% -493.88 -5.44% -169.28 -1.67% 0.95
Females 2,262 687 30.37% -364.63 -16.12% +10.19 +0.36% 0.86
Turf. Males 13,915 4,669 33.55% -788.15 -5.66% -60.48 -0.32% 0.94
Females 6,972 2,158 30.95% -603.22 -8.65% -87.89 -0.83% 0.92
All Weather. Males 8,271 2,880 34.82% -405.05 -4.90% -328.56 -2.62% 0.95
Females 3,309 990 29.92% -465.80 -14.07% +45.01 +0.82% 0.86


Part 2. All Weather Racing.

The tables in Part 2 cover All Weather racing only. I've tried to find areas where Fillies and Mares may be at a bigger disadvantage than what the figures in Table 1 would indicate.

When breaking down larger sets of figures the problem of unacceptably small sample sizes comes up. I've kept each category fairly general in order to keep the sample sizes at a reasonable level, however it was unavoidable to include sample sizes of less than 1,000 for some categories.

There are two sets of figures for each category. The first set of figures covers every runner, and the second set looks at the performance of the outright SP favourite when either Male or Female.

I've only looked at races that had at least a 25% representation of each sex. This was mainly to avoid the situation of sole females competing against a field full of males, and to have the playing field as level as possible. The trade off for this was having smaller sample sizes.

The Laying calculations used for the SP favourite sections of each table include Betfair commission of 5%.

All Weather Racing. Females v Males.
Tracks and Surfaces.
Surfaces.All Runners.
Surface. Sex Runners % Of Runners Races Wins. % Of Total Wins. A:E Figure.

Polytrack/Fibresand. Combined. Males 17542 58.80% 12651 8659 68.45% 0.89

Females 52897 41.20% 12651 4014 31.73% 0.75

Polytrack. Males 62644 58.73% 10440 7143 68.42% 0.89

Females 44102 41.27% 10440 3312 31.72% 0.74

Fibresand (Southwell). Males 12777 59.21% 2211 1516 68.57% 0.90

Females 8795 40.79% 2211 702 31.75% 0.79

SurfacesPerformance Of SP Favourite. 
Surface. Sex Runners Wins Win % PL @ SP ROI P/L Lay ROI Lay A:E Figure
Polytrack/Fibresand Combined. Males 8229 2863 34.79% -416.05 -5.06% -312.47 -2.41% 0.95
Females 3290 985 29.94% -461.21 -14.02% +45.62 +0.84% 0.86
Polytrack Males 6749 2317 34.33% -393.63 -5.83% -254.73 -2.37% 0.95
Females 2745 799 29.11% -432.39 -15.75% +71.18 +1.49% 0.84
Fibresand. Males 1481 546 36.87% -23.42 -1.59% -56.79 -3.34% 0.98
Females 546 186 34.07% -29.82 -5.46% -24.61 -3.63% 0.94
Tracks.All Runners.
Track. Sex Runners % Of Runners Races Wins. % Of Total Wins. A:E Figure.

Kempton Males 13332 59.00% 2206 1494 67.72% 0.88

Females 9217 41.00% 2206 716 32.28% 0.77

Lingfield. Males 18311 58.97% 3101 2200 70.94% 0.91

Females 12606 41.03% 3101 906 29.22% 0.72

Wolverhampton. Males 24407 59.00% 4161 2765 66.45% 0.88

Females 17201 41.00% 4161 1401 33.67% 0.80

Dundalk. Males 6334 56.00% 920 647 70.33% 0.88

Females 4893 43.00% 920 274 29.78% 0.56

Tracks.Performance Of SP Favourite. 
Track Sex Runners Wins Win % PL @ SP ROI P/L Lay ROI Lay A:E Figure
Kempton Males 1428 482 33.75% -34.72 -2.43% -119.32 -3.94% 0.97
Females 564 142 25.18% -138.80 -24.61% +75.01 +5.87% 0.75
Lingfield Males 2031 727 35.80% -60.78 -2.99% -77.55 -3.2% 0.96
Females 753 242 32.14% -58.97 -7.83% -19.85 -2.03% 0.92
Wolverhampton Males 2659 916 34.45% -213.25 -8.02% -58.88 -1.62% 0.93
Females 1105 337 30.50% -163.43 -14.79% -4.68 -0.29% 0.85
Dundalk Males 546 161 29.49% -79.37 -14.54% +2.50 +0.17% 0.88
Females 282 62 21.99% -77.64 -27.53% +34.18 +4.16% 0.70
Southwell. See Fibresand
Summary of Tracks & Surface figures.. I was expecting to find a bias towards either the Polytrack or the Fibresand, but the figures for both surfaces are almost identical.
The breakdown of the tracks that use the Polytrack surface don't seem to reveal any bias against a particular track either, with figures for all tracks very similar.
Females seem to have a poor record at Dundalk, but that could possibly be attributed to different race conditions for Fillies and Mares in Ireland, rather than the track surface, which is considered one of the better All-Weather surfaces.
Favourites, in general, had a lower than average record at Dundalk.
The green figures for laying female favourites at Kempton correlate with the below-average record of favourites in general at that track (the figures for males favourites at Kempton were also below Lingfield and Wolves).

With Kempton and Dundalk accounting for the green figures for laying female favourites on the Polytrack, my conclusion is that nothing much of value can be taken from the figures for Tracks and Surfaces.

All Weather Racing. Females v Males.
Distances.
All Runners.
Distance Sex Runners % Of Runners Races Wins. % Of Total Wins. A:E Figure.

5 Furlongs. Males 8175 54.70% 1612 1007 62.47% 0.89

Females 6780 45.30% 1612 607 37.66% 0.80

6 Furlongs. Males 14536 57.60% 2516 1688 67.09% 0.89

Females 10690 42.40% 2516 835 33.19% 0.77

7 Furlongs. Males 14908 58.90% 2409 1687 70.03% 0.90

Females 10392 41.10% 2409 726 30.14% 0.73

8 - 8.5 Furlongs. Males 15934 59.50% 2589 1812 69.99% 0.89

Females 10839 40.50% 2589 780 30.13% 0.73

9 - 10.5 Furlongs. Males 9914 59.30% 1574 1083 68.80% 0.88

Females 6820 40.70% 1574 492 32.20% 0.73

11 Furlongs +. Males 12933 61.55% 2115 1493 70.59% 0.89

Females 8076 38.45% 2115 628 29.69% 0.75

Performance Of SP Favourite. 
Distance. Sex Runners Wins Win % PL @ SP ROI P/L Lay ROI Lay A:E Figure
5 Furlongs. Males 893 309 34.5% -52.68 -5.9% -9.58 -0.75% 0.94
Females 448 159 35.49% +7.17 +1.60% -26.16 -4.00 1.00
6 Furlongs Males 1,502 532 35.42% -22.11 -1.47% -75.77 -3.42% 0.98
Females 583 188 32.25% -53.46 -9.17% -0.59 -0.06% 0.91
7 Furlongs Males 1,491 511 34.27% -84.23 -5.65% -106.66 -5.03% 0.95
Females 549 146 26.59% -128.44 -23.40% +50.00 +6.14% 0.75
8 - 8.5 Furlongs Males 1679 625 37.22% -21.95 -1.27% -105.01 -4.22% 0.99
Females 644 178 27.64% -108.69 -16.85% +12.35 +1.16% 0.81
9-10 Furlongs. Males 871 278 31.92% -81.40 -9.35% +6.62 +0.53% 0.89
Females 341 119 34.90% +6.59 +1.93% -66.24 -11.34% 1.04
11 Furlongs + Males 1,321 472 35.73% -61.72 -4.67% -57.67 -3.08% 0.96
Females 489 139 28.43% -113.56 -23.22% +38.15 +5.06 0.77
Summary of Distance figures.It will be interesting to follow the progress of these figures in the future.
They point to Females being at a bigger disadvantage over distances greater than 6 furlongs, especially in the 7 - 8.5 furlong range, while the 5 furlong figures show a much reduced bias against Females, if any, (although there were a higher percentage of Females contesting 5 furlong races than other distances).

Both sets of figures (for All Runners, and SP Favourites) seem to support each other.

All Weather Racing. Females v Males
Age Groups.
All Runners.
Age Sex Runners % Of Runners Races Wins. % Of Total Wins. A:E Figure.

2yo's. Males 12055 53.90% 2299 1479 64.33% 0.91

Females 10276 46.10% 2300 819 35.61% 0.76

3yo's (against 3yo's only). Males 14016 54.20% 2809 1810 64.44% 0.90

Females 11849 45.80% 2809 1006 35.81% 0.78 *
3yo's (against all age groups). Males 24200 52.20% 6721 3067 45.63% 0.91

Females 22167 47.80% 6922 1818 26.26% 0.77

4yo's (in races restricted to 4yo's+). Males 4948 49.50% 2250 593 26.36% 0.90

Females 5041 50.50% 2397 370 15.44% 0.74

5yo's (in races restricted to 4yo's+). Males 12793 71.38% 2714 1456 53.65% 0.90

Females 5128 28.62% 2403 333 13.86% 0.67

* Blindly laying all 3yo fillies in mixed sex races restricted to 3yo's has shown a profit of over 100 points at level stakes (with 5% commission) in each of the past 3 years
Performance Of SP Favourite. 
Age Sex Runners Wins Win % PL @ SP ROI P/L Lay ROI Lay A:E Figure
2yo. Males 1432 557 38.90% -58.90 -4.11% -16.03 -0.89% 0.96
Females 669 238 35.58% -53.40 -7.98% +11.68 +1.27% 0.92
3yo's (against 3yo's only). Males 1,776 662 37.27% -68.48 -3.86% -42.17 -1.81% 0.95
Females 795 237 29.81% -137.37 -17.28% +35.62 +2.80 0.83
3yo's (against all age groups) Males 930 357 38.39% +25.92 +2.79% -98.18 -6.33% 1.03
Females 581 168 28.92% -76.56 -13.18% -11.80 -1.08% 0.86
4yo's (in races restricted to 4yo+) Males 624 211 33.81% +5.19 +0.83% -79.01 -6,61% 0.99
Females 288 103 35.76% +23.37 +8.11% -45.22 -8.60% 1.03
5yo's+ (in races restricted to 4yo's+) Males 1,265 400 31.62% -60.24 -4.76% -68.83 -3.05% 0.94
Females 257 53 20.62% -77.61 -30.20% +41.55 +7.25% 0.69
Summary of Age Groups.

2yo and 3yo Fillies racing against their own age group have the lowest figures. Both are showing a positive return when laying at level stakes. 

This is consistent with the theory that females are at a physical disadvantage when racing against Males of a similar age. 

There were some interesting figures for 2yo's on a year to year basis (note below). The low figures for 5yo+ Mares are harder to explain. 

Because the sample size was low, I expanded the search to include every race (1-99% Males) for this age group, and checked the figures on a monthly and quarterly basis looking for any clues. 

They remained constant throughout the year however, with each Quarter and each Month having similar (lay) figures.

The positive backing figures at Industry SP for both Male and Female favourites as 4yo's in open company could be of interest, and possibly worth following for the future. 

Females as non-favourites in these races had a poor record.

I think there are clues amongst the figures for Age Groups that could shed some light on the bias against Females on the All Weather, and it is this category that interests me the most.


Year to year variance in the figures for 2yo fillies.

This category had the most erratic variance in fillies' performance from year to year.

  • From 2007 to 2012 the ratio of Males to Females in all Mixed Sex 2yo Races was fairly consistent at around 58:42 (range between 59:41, and 57:43).
  • There was an average of 296 Mixed Sex 2yo races per year (low 264, high 313).
  • Males dominated the races won by an average ratio of 68:32 (range from 65:35 to 70:30).
  • Blindly laying Fillies on a year to year basis produced the following P/L figures at level stakes:
  • 2007 +38.5
  • 2008 -106.8
  • 2009+21.1
  • 2010 +425.5
  • 2011 -2.4
  • 2012 +292.5

Interestingly, last year (2013) there was a sudden drop in the ratio of Males that contested 2yo Mixed Sex Races. 

The total number of Males dropped from the previous 6 year average of 1648, down to 1400, while the number of females contesting the same races stayed very close to the previous 6 year average of 1186 (at 1130). 

The ratio of Males to Females contesting these races last year was 55:45.

I haven't been able to explain this sudden drop in Males contesting Mixed Sex 2yo races on the All Weather yet, and may have to wait until the 2014 figures are available to see if this is an ongoing trend if no explanation can be found before then.



Just for interest, and to try and give some weight to the All Weather figures, I've cherry-picked a few of the categories and shown any significant staking calculations:

Criteria:

7 Furlongs. 5yo+ in Open Company. Favourites. Females. Mixed Sex Races with at least 1 male.

  • 13 Wins from 82 runners.
  • 15.85% SR
  • PL: (Laying) +13.82 points.
  • ROI: (Laying @Betfair SP with 5% commission) +10.05%

Criteria:

7 Furlongs. 2-3yo's. Favourites. Females in Mixed Sex Races with at least 1 male.

  • 133 wins from 514 runners.
  • 25.88% SR.
  • P/L: (laying) +35.90 points.
  • ROI: (laying) +9.76% (increases to 14% if racing against own age group).

Criteria:

6 Furlongs - 8.5 Furlongs. 5-6yo. Handicaps. Favourites. Females in Mixed Sex Races with at least 1 male.

  • 32 Wins from 200 runners.
  • 16% SR.
  • P/L: (laying) +48.42 points.
  • ROI: ( laying) 9.61%.

There are various other combinations that produce profitable laying calculations, mainly when using 2yo and 3yo races as a criteria, and there are no doubt other ways in which to exploit any bias.


Conclusion

The figures from the tables in this blog do show a bias against Fillies and Mares when racing against Males on the All-Weather.

I've looked at each category closer than the general tables show to try and find reasons for this, but I wasn't able to pinpoint anything of significance with the limited sample sizes available.

There were no clues when looking at the time of the year for example.

In general, the figures for 5 Furlong races showed less bias than any other category, and 7-furlong races had the largest bias.

Contrary to theories that I've heard and read, there doesn't appear to be any difference in bias between Fibresand and Polytrack. This could actually give a clue as to the type of Female racehorse that competes on the All-Weather. 

Is it possible that owners and trainers simply race inferior Fillies and Mares on the All-Weather surfaces? 

The bias is not as noticeable on the Turf, and the better Females are possibly kept for that type of racing.

This theory helps to explain a number of things that have bothered me since starting this research, and it's an idea that has grown on me the more time I spend looking at the results of this research.

I'm a fairly avid follower of betting systems, and I've noticed that when eliminating all females from system qualifiers the profitability of the system improves. 

This was also suggested by Flatstats.com in an article published by them 10 years ago.

I've got plenty of figures at hand from all the research that I've done for this blog, and if anybody reading this is interested in a closer look at any particular category feel free to PM me, and I'll help if I can. 

I'd like to publish the rest of the research that I have, and will probably do so when I've decided whether or not it's relevant for value betting and finding winners.

This subject of Females against Males has captured my interest, and I feel that it's worth more ongoing study to try and determine the exact cause of the apparent bias.

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