[AusRace] Jockeys generally
sean mclaren
seanmac4321 at gmail.com
Tue Jan 29 02:06:14 AEDT 2019
hello again
ignore the final column called Runs.
i had not cross checked.
the initial query for Dec 18 generated 16460 runs for trainers nationally.
the smiths were extracted as a sub set.
thanks
On Tue, Jan 29, 2019 at 12:49 AM sean mclaren <seanmac4321 at gmail.com> wrote:
> trainer state runs
> A D Smith NSW 5
> A D Smith WA 1
> B J Smith QLND 11
> B S Smith NSW 3
> B Smith QLND 3
> C Smith QLND 35
> D J Smith NSW 1
> D S Smith NSW 5
> D Smith NSW 2
> D Smith VIC 18
> F E Smith QLND 5
> G Smith QLND 2
> J Smith NSW 2
> K Smith NSW 1
> L Smith WA 3
> M J Smith ACT 6
> M Smith NSW 16
> Ms A J Smith WA 3
> Ms A Smith NSW 8
> Ms J Smith VIC 1
> P A Smith NSW 2
> P Smith NSW 1
> P Smith VIC 1
> R G Smith WA 3
> R Smith NSW 22
> S L Smith NSW 6
> hello Len
>
> national Dec 2018 numbers for the unique Smiths.
>
> 35 mins. total
>
> near 10 mins waiting for the query. Export to excel. Query, Sort, Format
> count etc etc
>
> i can offer no further comment.
>
> Thanks Sean
>
>
>
>
>
> On Mon, Jan 28, 2019 at 12:50 PM L.B.Loveday <lloveday at ozemail.com.au>
> wrote:
>
>> Easily via tables BUT " Agreed the work upfront is a tough but not
>> insurmountable". Tough indeed.
>>
>>
>>
>> Even with top trainers, I have problems as discussed earlier:
>>
>>
>>
>> D Hayes
>>
>> D J Hayes
>>
>> D & B Hayes & T
>>
>> David Hayes
>>
>> David Hayes & To
>>
>> D, B & T Hayes &
>>
>>
>>
>> G Waterhouse & A
>>
>> Ms G Waterhouse
>>
>> G & A Waterhouse
>>
>>
>>
>> WORSE, in a complementary data-base separately sourced, for only the last
>> 8 years, I have additionals:
>>
>>
>>
>> DHayes
>>
>> D&BHayes
>>
>>
>>
>> and:
>>
>>
>>
>> GWaterhouse
>>
>> G&AWaterhouse
>>
>> MsGWaterhouse
>>
>>
>>
>>
>>
>>
>>
>> Then what about this tiny non-random sample of 43 trainers out of the
>> 11,421 I have in my current (non-archived) data base?
>>
>>
>>
>> A D Smith
>>
>> A F Smith
>>
>> A J Smith
>>
>> A L Smith
>>
>> A Smith
>>
>> Ms A J Smith
>>
>> Ms A Smith
>>
>> Ms Alison Smith
>>
>> J A Smith
>>
>> J B Smith
>>
>> J C Smith
>>
>> J E Smith
>>
>> J L Smith
>>
>> J Smith
>>
>> Jeremy Smith
>>
>> Ms J M Smith
>>
>> Ms J Smith
>>
>> K C Smith
>>
>> K L Smith
>>
>> K M Smith
>>
>> K N Smith
>>
>> K R Smith
>>
>> K Smith
>>
>> K T Smith
>>
>> Kelvin Smith
>>
>> Ms K Smith
>>
>> L A Smith
>>
>> L C Smith
>>
>> L J Smith
>>
>> L R Smith
>>
>> L Smith
>>
>> Les Smith
>>
>> Ms L C Smith
>>
>> Ms L Smith
>>
>> M J Smith
>>
>> M K Smith
>>
>> M Smith
>>
>> M W Smith
>>
>> Marilyn Smith
>>
>> Matthew Smith
>>
>> Max Smith
>>
>> Melissa Smith
>>
>> Ms M Smith
>>
>>
>>
>> I can't be bothered looking up how many additionals there are in the
>> complementary 8-year file.
>>
>>
>>
>> How long do you figure it would take to be 99% sure you had determined
>> how many unique trainers are in the 43 names (and of course thus form a
>> tiny part of the required table)?
>>
>>
>>
>> Then do the same for at the other 11,378 names? Then match to the 8-year
>> file?
>>
>>
>>
>>
>>
>>
>>
>> *From:* Racing <racing-bounces at ausrace.com> *On Behalf Of *sean mclaren
>> *Sent:* Monday, 28 January 2019 8:54 AM
>> *To:* AusRace Racing Discussion List <racing at ausrace.com>
>> *Subject:* Re: [AusRace] Jockeys generally
>>
>>
>>
>> And I should add that names of jockeys or trainers can be easily overcome
>> in excel via look-up tables or in access via a table. The fuss escapes me.
>> Agreed the work upfront is a tough but not insurmountable. The challenge
>> for mine is placing a value on a jockey or a trainer that's in sync with
>> the scale of my type of performance rating. Which is why leaving them in
>> their raw state, as Roman does, is still quite appealing. Apart from its
>> simplicity, it shouldn't be ignored that a degree of randomness is created
>> by default and in a chaotic space (ie a horse Race) that could translate
>> into better prices because of unfashionable jockeys / trainers. Just some
>> thoughts.
>>
>>
>>
>> On Sun, 27 Jan 2019 13:55 Tony Moffat <tonymoffat at bigpond.com wrote:
>>
>> Roman – my response to Len wasn’t intended as having a shot at you, and
>> your assertion, but more to show that the values were aligning, at least in
>> the case of SGuymer and his 115/1.15.
>>
>>
>>
>> Personally, I like to involve the exposed values of runners engaged in
>> the upcoming race and minimize, if I can, the magical
>> addings/dividings/other things needed to construct a rank.
>>
>> Let’s call it evidence based handicapping.
>>
>>
>>
>> I do use the market – firstly, if you divide the place dividend by the
>> win dividend and rank the result you can see at a glance those runners
>> which have a disproportionate sum plonked for the win – my cut off value is
>> 41% - the place dividend is 41% of the win, which is the ‘normal’ range for
>> most out to $9, then the place div % falls away, the longer divs out there
>> in the badlands are being bet/hunted by somebody.
>>
>>
>>
>> Caulfield R7 yesterday – the one runner over 41% is 4AlGayel 48% from
>> $1.5/$2.5 – skinny I know but you get the gist.
>>
>> Caulfield R8 yesterday – the one runner over 41% is 8Manolo 50% from
>> $1.4/$2.8 – skinny etc.
>>
>> Randwic R9 yesterday - there are two selections over 41% - 1ST and 2ND
>> $1.80/$1.5
>>
>> Randwic R8 yesterday - the one runner over 41% is 8Sondelon 42% from
>> $1.4/$3.3
>>
>> Randwic R7 yesterday - there are two selections over 41% - Unp and Unp –
>> so it is not perfect.
>>
>> Randwic R6 yesterday - the one runner over 41% is 8Sei Stella 58% from
>> $1.5/$2.6
>>
>>
>>
>> SunCoast R8 yesterday- the winner was ranked 11, the 2nd was ranked 4,
>> and the 3rd was ranked 10th
>>
>> So it is not perfect.
>>
>>
>>
>> See the story of JIM, Jim and jim about scoring off these types of bets.
>> jim (all lowercase) has been known to move
>>
>> $1k on these until he accumulates his daily take – it was $1700 – and
>> never less than $1k if the pool is large (enough)
>>
>>
>>
>> I rank the quinella dividends then countif those runner numbers involved
>> in the first 10 – this may include up to 5 or more horses.
>>
>> My feeling was that, when I commenced doing that, that astuteness from
>> others caused them to select their bets and I could benefit from that.
>>
>> Those other punters had made an effort I considered, in isolation though.
>> Now the inclusion of flexi betting has affected that a lot but it still
>>
>> ‘seems to be’ a good strong lead. You need access to a matrix, not always
>> allowed now.
>>
>>
>>
>> I can do it with exacta divs as well – it is much of a muchness.
>>
>>
>>
>> All of the data above was from final dividends. In the sometimes frantic
>> betting scene before a race, with data changing 3 times a second, you have
>> to take a stab occasionally, and to hope that your selection holds it’s
>> value, they normally do.
>>
>>
>>
>> Cheers]
>>
>>
>>
>> Tony
>>
>> *From:* Racing [mailto:racing-bounces at ausrace.com] *On Behalf Of *Roman
>> *Sent:* Friday, January 25, 2019 5:51 AM
>> *To:* 'AusRace Racing Discussion List' <racing at ausrace.com>
>> *Subject:* Re: [AusRace] Jockeys generally
>>
>>
>>
>> Hi Tony,
>>
>> I respect the fact you have your way that works for you as Sean has his.
>> That’s how the punt goes for those keen enough to go past “pluck a duck”
>> with a cursory ten minute look or listen to various tipsters.
>>
>>
>>
>> The one constant I can quote is that the racetrack market is what I call
>> “linear”. I assume that’s the correct term where I mean favs win more times
>> than 2nd favs who win more than 3rd favs and so on. Thus the SP figure
>> is linear in that $2.50 chances win more than $4 chances who win more than
>> $7 chances and so on. I am sure we all agree that this general premise is
>> correct in the high 90’s percentage wise.
>>
>>
>>
>> Therefore, the rating of jockeys and trainers can be aligned to this
>> premise and their LOT or POT should give a reasonably clear picture of
>> whether they are up to the market assessment. Where this can go asunder a
>> fraction is that top trainers runners are overbet by a lazy public who
>> think the likes of Waller Hayes and Weir can train every favourite to win.
>> As most on this site realise many of their horses are “unders”.
>> Nevertheless that can be factored in.
>>
>>
>>
>> In the file I have DKWeir 7516 runners for minus 23.8%LOT, D Hayes 4710
>> for -17.6% LOT however at $3 or less Weir 1053 runners for -5.1% whilst
>> Hayes with 529 runners is -11.2%.
>>
>> So, if betting all odds, as I assume you do, you would use the larger set
>> you would credit Hayes with more points. The favs punters would give DKW a
>> better figure.
>>
>>
>>
>> These figures are, of course, open to all sorts of personal
>> interpretation if I add that overall from 7513 runners at $3 or less
>> covering all trainers the LOT is 8.1%. I am not sure but would 5.1 divided
>> by 8.1% give a figure or should it be vice versa.
>>
>>
>>
>> Naturally, a similar process for jockeys would find some riders of $3 or
>> less chances, for instance, better than others. From there some
>> jockey/trainer combos would be another facet i.e. Yendall/Weir, Allen/Weir,
>> Bowman/Waller et al but a downside for some combos would be not enough runs.
>>
>>
>>
>> However, all said above is just one way!!
>>
>>
>>
>> Cheers
>>
>> Roman
>>
>>
>>
>> *From:* Racing [mailto:racing-bounces at ausrace.com
>> <racing-bounces at ausrace.com>] *On Behalf Of *Tony Moffat
>> *Sent:* Friday, January 25, 2019 1:59 AM
>> *To:* racing at ausrace.com
>> *Subject:* Re: [AusRace] Jockeys generally
>>
>>
>>
>> Len – thanks
>>
>>
>>
>> Kozzi’s assertion that the iv are poles apart does not hold up here – I
>> have similar scores to yours. I leave mine at 1.15 for Guymer and you
>> promote him by multiplying by a hundred (de-decimate?) to get 115 (I guess).
>>
>>
>>
>> I wanted a score in the here and now and that is how/why I came to derive
>> the iv, it is contemporaneous with other riders in this race, their
>> presence affects its score somewhat, a little, and never majorly. It is not
>> uniquely mine, by the way. It involves the use of all the placings, I had
>> included the win record only, then added second place(s) to see how that
>> ran and have reverted to this input now.
>>
>>
>>
>> My calculations are in the mould of ‘ok, what can you do’, looking
>> forward, and others can be described as ‘look what I done’.
>>
>>
>>
>> The inclusion of performance at price bands might be the best but I don’t
>> have that data, the prices of past endeavors. I can access it, the prices,
>> but choose not to manually enter it, and who would do that.
>>
>>
>>
>> Yes, I do iv for jockeys (as you know) and also trainer, horse, distance
>> and form and multiply these to get a value for each runner – highest is
>> best.
>>
>>
>>
>> Form is a two part process. I involve their last 4 runs by multiplying
>> the places together, remove the worst result, then rank that – this appears
>> to be strong information, and has always been.
>>
>>
>>
>> As a factor in a weight rating process used, I again involve their places
>> but this time I start from a base of 9 (the worst there can be) then
>> subtract each succeeding run from the previous product until I get a score
>> from which I can calculate a rating to win. So 6214, comes out as
>> -3,-4,-1,3 and when summed this is -3+-4+-1+3 = -5. The -3(minus three)
>> came from 6-9 = -3, the -4 (minus 4) came from 6-2 = -4, the -1 (minus 1)
>> came from 2-1 =-1, and the (+)3 came from 4 minus 1 = +3. The -5 for this
>> runner, and the calculated scores for all runners is then multiplied by 1.5
>> to give a weight rating variation and this product is then added to the
>> limit weight for this race and the allocated weight deducted from that. The
>> best result, the highest/biggest number resulting from that is considered
>> the best for this race, and you can zero that against the other calculated
>> weights to sort out the weight rated best ranking.
>>
>>
>>
>> I use a variation of this method in my own punting, having streamlined a
>> few of the calculations, but the principles are the same, and the
>> selections also. I back more than one runner in each chosen race, often a
>> quinella now, and for several years, with a saver on some of the quinella
>> inclusions.
>>
>>
>>
>> I don’t use or include the iv selections in my punting yet, I may do
>> soon, and include it here only for information and comment.
>>
>>
>>
>> Cheers
>>
>>
>>
>> Tony
>>
>>
>>
>> FROM THE ARCHIVES
>>
>> From: ausrace-bounces at ausrace.com [mailto:ausrace-bounces at ausrace.com
>> <ausrace-bounces at ausrace.com>] On Behalf Of Nick at Twonix
>>
>> Sent: Thursday, 5 November 2015 1:29 PM
>>
>> To: 'AusRace Mailing List' <ausrace at ausrace.com>; 'L.B.Loveday'
>>
>> <lloveday at ozemail.com.au>
>>
>> Subject: Re: [AusRace] Michelle Payne
>>
>>
>>
>> I did an analysis of 271 K Aus races rides over last 2-3 years and
>> discovered that Male jockeys have a 2% better strike rate and a 3% better
>> A2E (think POT betting to prices).
>>
>> However Apprentices ( both Male and Female) have the same Strike Rate and
>> A2E . Licensed Male jockeys have a 6% better A2E compared to Female jockeys.
>>
>>
>>
>> Category Rides Wins S/Rate ExpW
>>
>> A2E
>>
>> Aus Races 271,662 35,340 13% 40,474 -13%
>>
>> Female 40,478 4,626 11% 5,448
>> -15%
>>
>> Apprentice 21,840 2,549 12% 2,930 -13%
>>
>> Licensed 18,638 2,077 11% 2,518
>> -18%
>>
>> Male 231,184 30,714 13% 35,026
>> -12%
>>
>> Apprentice 54,329 6,789 12% 7,840 -13%
>>
>> Licensed 176,855 23,925 14% 27,186 -12%
>>
>>
>>
>> AN
>>
>>
>>
>> Len, I was able to distinguish Female jockeys in AAP data as they all
>> start with "Ms ". I am assuming that MS Dhoni doesn't ride in Aus :-)
>>
>>
>>
>>
>>
>> *From:* Racing [mailto:racing-bounces at ausrace.com
>> <racing-bounces at ausrace.com>] *On Behalf Of *L.B.Loveday
>> *Sent:* Tuesday, January 22, 2019 9:50 AM
>> *To:* 'AusRace Racing Discussion List' <racing at ausrace.com>
>> *Subject:* Re: [AusRace] Jockeys generally
>>
>>
>>
>> "Raw wins and wins and placings" don't mean much in absence of prices -
>> it's easy to back winners; just back every runner at 1/1 or less and you'll
>> back around 56% of winners, and "just" lose about 5.5%.
>>
>>
>>
>> Nor is just looking at past returns enough - factors such as those you
>> list, and eg, track, trainer should be considered.
>>
>>
>>
>> Here's a simplistic look at some figures that could be used:
>>
>>
>>
>>
>>
>> Considering the last 1000 rides for jockeys who have had at least 1000
>> rides in the past 14 years (a somewhat different picture arises if only
>> considering since the advent of SOP rather than traditional SP as SOP
>> markets have lower market%s, especially away from Sydney/Melbourne tracks):
>>
>>
>>
>> Best returns @ SP:
>>
>>
>>
>> SThornton 101
>>
>> MJWalker 103
>>
>> WD'Avila 103
>>
>> CParnham 104
>>
>> VWong 104
>>
>> DMoor 105
>>
>> PWells 105
>>
>> DWBallard 107
>>
>> SFawke 113
>>
>> SGuymer 115
>>
>> JOliver 117
>>
>>
>>
>> Considering only rides on horses "in the market" - gets rid of outliers
>> like 125/1 winners:
>>
>>
>>
>> JPStanley 100
>>
>> JPracey-Holm 100
>>
>> JTaylor 100
>>
>> MWeir 100
>>
>> RFradd 100
>>
>> RonStewart 100
>>
>> KWalters 102
>>
>> SLisnyy 102
>>
>> LJMeech 103
>>
>> TPannell 103
>>
>> CGallagher 104
>>
>> RMaloney 106
>>
>> CHall 107
>>
>> BWerner 108
>>
>> DWBallard 108
>>
>> JLyon 109
>>
>> PWells 109
>>
>> SThornton 109
>>
>> CNutman 110
>>
>> VBolozhinsky 112
>>
>>
>>
>> Worst returns @SP:
>>
>>
>>
>> LGHenry 21
>>
>> JeffKehoe 31
>>
>> DPitomac 33
>>
>> TJeffries 33
>>
>> SBayliss 34
>>
>> JMissen 36
>>
>> MJStephens 37
>>
>> ABadger 38
>>
>> NRose 38
>>
>> SStarley 38
>>
>> ECockram 39
>>
>> JKeating 39
>>
>> MHackett 39
>>
>> RYetimova 39
>>
>> SParnham 39
>>
>>
>>
>>
>>
>> Considering only rides on horses "in the market" (as I've previously said
>> LGHenry is in a class of her own):
>>
>>
>>
>> LGHenry 27
>>
>> MJStephens 32
>>
>> SBayliss 36
>>
>> CBryen 41
>>
>> JMissen 43
>>
>> SGalvin 45
>>
>> SStarley 45
>>
>> ABadger 46
>>
>> DPitomac 46
>>
>> BPowell 47
>>
>> MHackett 47
>>
>> SParnham 47
>>
>> BStower 48
>>
>> PaulPayne 49
>>
>> CQuilty 50
>>
>>
>>
>> The big gaps - All "in market"
>>
>>
>>
>> SFawke 113 79
>>
>> WD'Avila 103 75
>>
>> MJWalker 103 76
>>
>> JOliver 117 91
>>
>> BMertens 88 63
>>
>>
>>
>> JTaylor 69 100
>>
>> NPunch 60 95
>>
>> JeffKehoe 31 72
>>
>> SLisnyy 61 102
>>
>> CHall 66 107
>>
>> VBolozhinsky 70 112
>>
>>
>>
>>
>>
>>
>>
>> *From:* Racing <racing-bounces at ausrace.com> *On Behalf Of *Roman
>> *Sent:* Monday, 21 January 2019 9:34 PM
>> *To:* 'AusRace Racing Discussion List' <racing at ausrace.com>;
>> tonymoffat at bigpond.com
>> *Subject:* Re: [AusRace] Jockeys generally
>>
>>
>>
>> Hi all,
>>
>> The fascination of it all is that two raters could have the same jockey
>> literally poles apart depending on criterias chosen.
>>
>>
>>
>> I have never rated jockeys nor trainers as I wonder if there is all that
>> much between a number of them at the top level. If the SP figures is a
>> solid determinant of the overall structure of horse racing does it not
>> figure those jockeys that ride well on well fancied horses are giving the
>> horses the chance of winning the market determines. Say Jockey A has 100
>> rides in races in town on favs and scores 35% of the time is he not a
>> fraction better than Jockey B who rides 32%. So the next time the two
>> jockeys meet on say favs at 2/1 and 9/4 (close) but the 32% jockey rides an
>> on pacer and the 35% jockey rides a chronic get back type where does the
>> ratings look now. It would be best to rate them all on their ability with
>> leaders, on pacers, mid fielders and get back types and another set of
>> figures comes up far more accurate, imho, than just a raw wins and wins and
>> placings.
>>
>>
>>
>> I look forward to Len’s reply.
>>
>>
>>
>> Roman Koz
>>
>>
>>
>> *From:* Racing [mailto:racing-bounces at ausrace.com
>> <racing-bounces at ausrace.com>] *On Behalf Of *L.B.Loveday
>> *Sent:* Monday, January 21, 2019 6:12 PM
>> *To:* tonymoffat at bigpond.com; racing at ausrace.com
>> *Subject:* [AusRace] Jockeys generally
>>
>>
>>
>> Tony,
>>
>>
>>
>> Did not get to me and I just saw it in the archives - a very different
>> rating method to mine; I'll evaluate and comment anon.
>>
>>
>>
>> LBL
>>
>>
>>
>>
>>
>> 790*150-93-96 is the revealed racing stat for Linda Meech tomorrow - to
>>
>> expand this Ms Leech has had 790 rides for 150 wins in the time frame
>>
>> covered by this stat. My IV for that is 1.4, essentially she is 40%
>> advanced
>>
>> on some others in this race.
>>
>>
>>
>> No rider gets less than 1, although the calculation is often less than
>>
>> that, John Keating has .6 (scores a one in the scheme). Why? - he is on a
>>
>> horse in the race and Bradbury's have happened, although I use the 1 for
>>
>> statistical pureness, and to get rid of some decimals. To be factual, off
>> a
>>
>> calculation, Keating is somewhere like 80% more unlikely of producing a
>> good
>>
>> ride than Meech - he has 395*17-25-33 and is .6 against Meech at 1.4 (1.4
>> -
>>
>> .6 is the basis of the claim for 80%).
>>
>>
>>
>> Jason Maskiell is also on 1.4 in this race, off 347*54-46-41. The factor
>> is
>>
>> 0.300552251 (the average of all jockeys riding) and my fall back value is
>>
>> .31 - if a jockey can't be rated (the data is missing e.g.) then I assign
>>
>> that value to it early in the calculation.
>>
>>
>>
>> Roger Biggs wrote that he used .2595, which may be the statistical base of
>>
>> all jockey placings across many rides. This has changed somewhat, there
>> is a
>>
>> jockey db. on RB Ratings. I am unaware of another method to rate and rank
>>
>> jockeys against all their rides. They can only ride one horse in a race so
>>
>> that the iv concocted from a large number of rides seems to be correct,
>> and
>>
>> I total all the rides for all jockeys in the race then divide that into
>> all
>>
>> the places achieved by all the jockeys, and from that sub-total I
>>
>> individually determine an iv.
>>
>>
>>
>> There is a place system for ranking jockeys when on favorites, but that is
>>
>> not the jockey at all. Another time perhaps. Who likes, or wants,
>> dividends
>>
>> in the sub $2 range, most of us really.
>>
>>
>>
>> This upcoming race has riders which have achieved 4708 rides totally under
>>
>> the period of review, and of those rides those riders scored, placed, in
>>
>> 1415. So, 1415/4708 = .300552251 is the factor to be used. Individually
>>
>> Keating has 395*17-25-33 (17+25+33/395 = .1898734) and this product is
>> again
>>
>> divided by the total score .3005522512 to give the score of .6. These
>>
>> numbers seem minimal, mickey mouse almost, but are a significant part of
>> the
>>
>> overall stat picture
>>
>>
>>
>> Trainers may have two or more runners in the race. I score them the same
>> as
>>
>> jockeys, total rides into total places (123) and develop a iv score from
>>
>> that.
>>
>>
>>
>> Involving riders and trainers, getting a score from them combined, I
>>
>> multiply their ivs and work with the product, ranking that.
>>
>> Meech 1.4, trainer 1.3 (1.4 * 1.3 = 1.82)
>>
>> Keating 1, trainer 1 (1 * 1 =1) actually .6 * .1. The trainer is yet to
>> win
>>
>> a race
>>
>> Maskiell 1.4, trainer 1 (1.4 * 1 = 1.4.
>>
>> Dylan Dunn = 1.1
>>
>>
>>
>> There is some upside to Linda Meech ability, trainer ability.
>>
>> This is R2 Kyneton tomorrow, a maiden and I'm not betting
>>
>> in it, nor do I suggest you do.
>>
>>
>>
>>
>>
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