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donnynz
Helper II
Helper II

Dax code to calculate a value above the minimum value

Hello, this is my first time posting so hopefully I provide the right information to get help on an area I need to improve, and that's DAX coding.

 

I have a report based on horse racing and one measure I want to include is for each race and for each horse how much weight is it carrying above the minimum. So for example Horse A carrying weight is 57.5, Horse B 56.0, Horse C 55.0 and Horse D is 52.5. I want a column that will show the weight being carried above the minimum weight for the selected race. So based on the above it should show Horse A 5.0, Horse B 3.5, Horse C 2.5 and Horse D 0 (as this is the lowest weighted horse). 

 

I've tried a few things around DAX code but get the same weight the horse already shows as carrying or all show 0, very frustrating.

 

Any help is much appreciated.

 

Thanks

 

Brendan 

1 ACCEPTED SOLUTION

I'm trying to add a new measure to the table 'Race Data#1' but get the following message:

 

The column 'Neurals Mstr[Race No.]' either doesn't exist or doesn't have a relationship to any table available in the current context.

 

The two tables 'Race Data#1' and 'Neurals Mstr' have a relationship based on horse name as that is common in both. I also have date as common in both tables. I'm wondering if that is what is causing the error message as the DAX is about Race No. and Carry Weight which are not used as the relationship connector?

 

Thanks

View solution in original post

42 REPLIES 42
Jos_Woolley
Solution Sage
Solution Sage

Don't add the column within Power Query! You should be adding that column to your table within DAX.

Cheers

Hi Jos, I finally got it!!! I added the column BUT it returns all 0s in the column for weight above the minimum.

 

donnynz_0-1635143343883.png

 

Jos_Woolley
Solution Sage
Solution Sage

Ok, for starters this isn't a measure, it's a calculated column. And the error you're getting suggests to me that you're attempting to put the calculated column I provided in the wrong table. I suggest you go back to post #10 in this thread and check very carefully the information you posted, especially the table names.

Regards

Hi Jos,

 

Sorry to bother you but had an update. The first table after ALLEXECPT was 'neural mstr' but changed it to 'All Data#1'. Doing this (as per below) now produces data.

 

Weight above Minimum =
VAR RaceNo =
RELATED ( 'Neurals Mstr'[Race No.] )
RETURN
'Race Data#1'[Carry Weight]
- CALCULATE (
MIN ( 'Race Data#1'[Carry Weight] ),
ALLEXCEPT ( 'Race Data#1', 'Neurals Mstr'[Race No.] ),
'Neurals Mstr'[Race No.] = RaceNo
)
 
The only issue is some races it returns the weight above minimum as expected but for races it is wrong.
 
RIGHT
donnynz_0-1635234385074.png

WRONG

donnynz_1-1635234466698.png

Do you have any idea why the second table shows the minimum weight incorrectly?

 

Thanks

I'm afraid I wouldn't be able to tell you unless I saw the precise data from the two tables as well as the exact version of the formula you are using. If the real table data is too big to give here, then you'll have to create reduced versions which exhibit the issue you're having. 

Regards

Happy to provide the datasets but can I forward the excel attachments?

Can you not just paste small versions of your tables which give the incorrect results you describe?

Regards

 

Hi Jos,

Is the model screenshot of any use? I can share the neural mstr workbook dataset details but struggling to figure out how to show the Race Data#1 dataset. I get this in an excel workbook but make transformations using power query and don't have power bi pro so can't figure out how i can share it.

donnynz_0-1635492469257.png

 

I don't think the relationship is the issue here. And I'm still not sure why you can't just create two small, mocked-up versions of your tables which exhibit incorrect results similar to the ones you are experiencing in your real workbook.

Regards

Hi, I've managed to make a sample set for one of the races that are showing as incorrect. To follow is the current output in Power BI and then the two datasets used for the output.

 

donnynz_0-1635503743397.png

 

NEURAL MSTR dataset

Horse No.HorseJockeyBarrierTrainerWeight Performance (algo)Career Performance Assessment (based on weight/class algo)Current Form (measured by class/weight algo)Revolutionary Time Assessment (adjust algo)Jockey Ability (algo)Trainer Ability (algo)Jockey/Trainer Combination (algo)Barrier Position (algo)Wet Track Performance (algo)Course Suitability (algo)Distance Suitable (algo)Prizemoney Earned (algo)Days since Last Run (algo)HCPNeural RatingPrice RatingRace No.CourseDateRace TimeRace NameDistanceClassPrize MoneyStartersTrack Conditions
5SOLAR APEXTOMMY BERRY6CHRIS WALLER57.51117520282015082521150184.5$3.702Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0008Good
7FORTIFIEDBROCK RYAN5CHRIS WALLER55.52920145288130121321200182$4.402Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0008Good
2MIGHTYBEELHUGH BOWMAN8CHRIS WALLER59.51115718288110261522180177.5$5.002Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0008Good
4MARGIE BEEJOSH PARR2KRISTEN BUCHANAN57.55131310333120112014160149$7.002Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0008Good
6LORD ARDMORELEE MAGORRIAN1CHRIS WALLER57.52411102810130121212200142.5$8.502Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0008Good
8DINADOJASON COLLETT4R & M FREEDMAN5441681083150121013200118$12.002Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0008Good
3PROMPT PRODIGYGLYN SCHOFIELD3EDWARD CUMMINGS59511310814016121413096$16.002Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0008Good
9APACHE BELLEKATHY O'HARA7WAYNE SEELIN524913339044818064.5$31.002Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0008Good
1MILK MANJENNY DUGGAN-MATTHEW SMITH611121151015102913418160scrscr2Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0008Good

 

RACE DATA#1 dataset

Horse No.HorseWeightRecent FormBarrierBP AdjJockeyJockey AllowanceTrainerAgeSexJockey RatingTrainer RatingRuns Since Last WinDays Since Last RunWeight ChangeDistance ChangeAverage Prizemoney - CareerAverage Prizemoney - Last 12mHighest Winning WeightDegree of DifficulityJockey & Horse WinsJockey & Horse PlacesJockey & Horse StartsJockey & Horse Win%Jockey & Horse Place%Career WinsCareer PlacesCareer StartsCareer Win%Career Place%Career Last 12m WinsCareer Last 12m PlacesCareer Last 12m StartsCareer Last 12m - Win%Career Last 12m - Place%Course WinsCourse PlaceCourse StartsDateCarry WeightCareerWeight above MinimumMinimum weight should be
2MIGHTYBEEL59.54x41187H BOWMAN C WALLER4G2.54.3010-1.0100$10,245$11,27860.52.5000003293356328386210130-Oct-2159.53-2-987.5
3PROMPT PRODIGY59.00x79838G SCHOFIELD EDWARD CUMMINGS4H1.92.67284.0300$6,477$6,88957.56.0001001514743141284201130-Oct-21591-5-147.57
4MARGIE BEE57.50316222J PARR K BUCHANAN6M2.53.0215-1.5300$6,469$8,62258.52.0001008134319494719215800130-Oct-2157.58-13-4365.5
5SOLAR APEX57.50x11365TOMMY BERRY C WALLER4G4.34.31210.5-100$9,668$10,68159.00.50330100381323853611278201130-Oct-2157.53-8-1365.5
6LORD ARDMORE57.590x5711L MAGORRIAN C WALLER4G2.54.387-0.5300$5,422$6,72056.03.00010011119180080000030-Oct-2157.51-1-1165.5
7FORTIFIED55.58166354BROCK RYAN2C WALLER4G2.24.333-4.00$9,554$9,55459.0-1.000000431233584312335800030-Oct-2153.54-3-1221.5
8DINADO54.05582143J R COLLETT R & M FREEDMAN5M2.93.307-1.0300$5,825$6,81255.51.000000452317392314143600030-Oct-21544-5-232.52
9APACHE BELLE52.01534576MS K O'HARA W SEELIN6M1.82.5410-5.0350$3,756$5,23059.52.5014025793818424416255000130-Oct-21527-9-380.50

 

Hopefully this all helps. The last column in the Race Data#1 dataset is what i've added to show what the output should be. Again thanks for the help.

Ok, thanks. I've just taken those two tables, imported them into a completely new, blank report, then entered a Calculated Column within the Race Data#1 table with precisely the same formula as I gave in post #11 and I get precisely the same results as your Minimum weight should be column. I have no idea how you've ended up with the results in the Weight above Minimum column.

Regards

RACE DATA#1 (race 4)

1BAROSSA ROSA61.00x3101515J DUGGAN2T BARTLEY4M2.92.61144.0100$12,533$14,42958.00.50000034112764339336710130-Oct-21593-4-117.57.5
2DALAALAAT60.028x141414L MAGORRIAN NATHAN DOYLE5G2.82.51352.0200$8,039$10,26958.51.0102505035142157228255000130-Oct-21603-5-148.58.5
3TAMPERING60.0x114388J BYRNE K PARKER5G4.22.62424.0-100$7,137$8,14658.51.000000562619422113152300130-Oct-21605-6-268.58.5
4DIFFERENT STROKES59.52121111H BOWMAN K WAUGH4G2.71.30213.5100$17,232$17,23259.00.0202100100336501003365010000030-Oct-2159.53-3-688
5GRACE BAY58.539x151313J PARR CLAIRE LEVER5M2.51.9110-0.5200$8,221$10,25261.01.00000053133862429446700030-Oct-2158.55-3-1377
6HIGHLY DESIRED58.5437x177MS K O'HARA CLAIRE LEVER7G1.71.90210.5200$5,750$6,30462.0-1.00000078322247115204000030-Oct-2158.57-8-3277
7REBEL RAMA58.0319x122R DOLAN M CONNERS4M2.51.90170.0100$9,731$12,30458.00.000000331323463310306010130-Oct-21583-3-136.56.5
8CREAM RISES58.014x801616N RAWILLER K GAVENLOCK4G3.51.83212.0100$8,296$8,29659.04.0001002054040205404000030-Oct-21582-0-56.56.5
9TOO GOOD TO BE TRU58.0724x71111A ADKINS D LANE4G1.82.45212.0100$6,558$6,55857.56.000000321030503210305000130-Oct-21583-2-106.56.5
10SUNBORN57.55131320-G SCHOFIELD EDWARD CUMMINGS4M2.52.61381.5-100$10,640$7,74758.54.0104252532132338226336700330-Oct-2157.53-2-13  
11DARALINA BELLE57.0111266TIM CLARK M SMITH4M2.42.8117-1.0100$11,448$11,44858.0-0.500000314751003147510000030-Oct-21573-1-45.55.5
12CONRAD55.517x1399ALYSHA COLLETT K WAUGH3G3.01.3110-5.0100$11,293$11,29357.53.001101002254080225408000130-Oct-2155.52-2-544
13ALL MACHIAVELLIAN55.03110121-J FORD G HICKMAN3G2.82.9014-3.0-100$9,153$9,15358.00.0000003165067316506700130-Oct-21553-1-6  
14KOBESTAR55.039x211818TOMMY BERRY R & L PRICE4G4.41.9023-4.0300$4,563$6,34859.02.520210010023151333239225600130-Oct-21552-3-153.53.5
15DUFRESNE54.52621655TYLER SCHILLER3A CUMMINGS3G2.82.917-2.5-100$10,365$10,36557.5-4.5000001252060125206010130-Oct-2151.51-2-500
16KISS SUM54.01x16544J R COLLETT T BARTLEY3G1.82.6210-3.5100$6,237$6,23757.54.010333332163350216335000130-Oct-21542-1-62.52.5
17DIVINE BREATH53.529x221010BROCK RYAN2T BATEUP4M2.21.95211.5100$6,523$7,68355.50.501101001581275136176700030-Oct-2151.51-5-800
18VERBEK53.06x22333JEAN VAN OVERMEIRE R & L PRICE3G1.61.9010-3.5100$6,859$6,859-0.50000006807506807500130-Oct-21530-6-81.51.5
19CANYONERO61.0134131919  RICHARD LITT6G-2.51145.0100$7,184$8,51160.02.5000006194414573415204701130-Oct-21616-19-44  
20CAESARS PALACE57.505x311212BRODIE LOY A CUMMINGS4G2.02.909-2.50$5,412$6,85160.03.00000033132346329335600130-Oct-2157.53-3-1366
21DOM TYCOON56.5141151717  PETER ROBL4G-2.3110-1.0-50$4,785$5,03759.01.500000441625504415275300030-Oct-2156.54-4-1655
22HEZA GENTLEMAN57.0118392220  M & D KEARNEY5G-2.43145.0100$3,415$4,60755.02.000000493511372515134700030-Oct-21574-9-35  

RACE DATA#1 (race 3)

1BRANDERS RULE60.07x16118-JACK MARTIN MITCHELL BEER4G1.81.7022-1.525$6,015$6,51561.51.5000003083838204505000030-Oct-21603-0-8  
2ICE IN VANCOUVER60.0173x022TYLER SCHILLER3C MORGAN4G2.43.63145.00$10,614$11,50661.0-2.0000003174357114255000030-Oct-21573-1-754
3CASINO KID58.0212x61111ALYSHA COLLETT J BOWEN4G2.82.02140.5100$6,716$8,39358.01.000000371718592410206000130-Oct-21583-7-1765
4CEASEFIRE58.017x1033R DOLAN P MESSARA4G2.32.41143.00$10,161$10,16159.5-1.5001003056060305606000030-Oct-21583-0-565
5PRIVATE AGENT57.02x3311414BRODIE LOY K GOLDMAN4G2.91.5014-2.50$7,163$8,22759.50.50000023633831233310000030-Oct-21572-3-654
6BLOW DART56.518x131010TIM CLARK B THOMPSON3G3.51.51141.0100$9,240$9,24059.0-0.501101003265083326508300130-Oct-2156.53-2-64.53.5
7BELLASTAR56.5528x066A ADKINS COLT PROSSER6M1.51.711140.5100$5,205$4,46758.06.0000003622144102603300130-Oct-2156.53-6-224.53.5
8TEJORI56.0280x511TOMMY BERRY N OLIVE5M4.21.88101.0200$7,646$3,89057.55.00120503312255002504000030-Oct-21563-3-1243
9BRAVE ENOUGH56.05x35177J DUGGAN2C MORGAN5G2.43.608-3.0100$4,401$3,50959.00.00000033161938128123800030-Oct-21543-3-1621
10CHEVCONI55.565x1199J PARR G WILLIAMS5G3.62.6015-2.0-8$5,666$5,33859.01.0000003183850206333300030-Oct-2155.53-1-8  
11BEAN HOT55.051x3144L MAGORRIAN BRETT ROBB4G2.72.8020-2.0100$6,955$6,95558.5-0.5000003383875338387500030-Oct-21553-3-832
12CLOUD FACTORY55.05441x55MS K O'HARA S EDWARDS5G2.32.003490.0-200$4,347$6,15059.0 0000031122533102505001130-Oct-21553-1-1232
13KING'S TRUST55.0030x81716BROCK RYAN2T ROBINSON6G3.43.210140.5100$5,886$5,90856.54.00020024191132121282501430-Oct-21532-4-1910
14SHELBY SIXTYSIX55.04x7031515J R COLLETT D J WILLIAMS5G2.81.8714-5.00$4,880$2,67558.50.0000002511186403605000130-Oct-21552-5-11  
15ALL FORMIDABLE54.52104188J INNES JNR K DRYDEN4G2.61.708-3.50$5,117$5,11758.02.5000002163350216335000030-Oct-2154.52-1-62.51.5
16TESTATOR SILENS54.51x11313JEAN VAN OVERMEIRE LUKE CLARKE4G1.82.1042-4.5200$11,740$11,74059.00.50000020210010020210010000030-Oct-2154.52-0-22.51.5
17THE SNOOPERSTAR54.52040x16-  MARK SCHMETZER5G-2.511154-1.0-200$3,265$1,83359.0 000003625123602902200330-Oct-2154.53-6-25  
18RAPIDITY54.0x14131212J FORD JAMIE STEWART6M1.72.4141-2.0-200$3,359$4,62358.01.0011010036191647249226700030-Oct-21543-6-19  

RACE DATA#1 (Race 1-2)

Horse No.HorseWeightRecent FormBarrierBP AdjJockeyJockey AllowanceTrainerAgeSexJockey RatingTrainer RatingRuns Since Last WinDays Since Last RunWeight ChangeDistance ChangeAverage Prizemoney - CareerAverage Prizemoney - Last 12mHighest Winning WeightDegree of DifficulityJockey & Horse WinsJockey & Horse PlacesJockey & Horse StartsJockey & Horse Win%Jockey & Horse Place%Career WinsCareer PlacesCareer StartsCareer Win%Career Place%Career Last 12m WinsCareer Last 12m PlacesCareer Last 12m StartsCareer Last 12m - Win%Career Last 12m - Place%Course WinsCourse PlaceCourse StartsDateCarry WeightCareerWeight above MinimumMinimum weight should be
1GEORGIE'S PRIDE61.019x3322J BYRNE D FORSTER4M4.13.23147.00$15,579$21,95059.03.5438508864144371539568900030-Oct-21616-4-14108
2KATALIN59.00x15x33N RAWILLER JAMES CUMMINGS5M4.43.711330.0-100$11,669$13,63858.0 000003065050102505000130-Oct-21593-0-686
3SELBUROSE59.00322177H BOWMAN C WALLER4M3.54.10562.5100$13,654$16,67557.50.000000361421643310306000130-Oct-21593-6-1486
4SELEQUE57.01244x44TOMMY BERRY C WALLER4M4.24.1366-2.5-50$8,612$10,60559.0 114255035122567328386200030-Oct-21573-5-12  
5JUST FIELD56.52290511J PARR D LANE5M3.62.69211.5200$6,728$7,21859.53.502306735221436121192701230-Oct-2156.53-5-225.53.5
6NEWSREADER56.51x0x855TIM CLARK J O'SHEA4M3.54.32260.50$20,221$34,32555.55.50000023102050205404000230-Oct-2156.52-3-105.53.5
7BOWERY BREEZE56.52682588MS K O'HARA P & M CAVE5M2.31.95142.0-200$8,876$11,40056.52.033132346472417462313153800030-Oct-2156.54-7-245.53.5
8LADY BROOK56.04x12566TYLER SCHILLER3J PRIDE5M2.43.5217-4.0100$7,142$7,27960.05.02036767551436713310306000030-Oct-21535-5-1420
9ZOROCAT56.02137499J R COLLETT D LANE4M2.82.63170.50$10,595$13,03956.56.01133367371520672510207001230-Oct-21563-7-15  
1MILK MAN61.0402319-J DUGGAN M SMITH7G2.52.10173.0300$8,599$5,89158.01.0101100100410301347131193600430-Oct-21614-10-30  
2MIGHTYBEEL59.54x41187H BOWMAN C WALLER4G2.54.3010-1.0100$10,245$11,27860.52.5000003293356328386210130-Oct-2159.53-2-97.57.5
3PROMPT PRODIGY59.00x7983-G SCHOFIELD EDWARD CUMMINGS4H1.92.67284.0300$6,477$6,88957.56.0001001514743141284201130-Oct-21591-5-14  
4MARGIE BEE57.50316222J PARR K BUCHANAN6M2.53.0215-1.5300$6,469$8,62258.52.0001008134319494719215800130-Oct-2157.58-13-435.55.5
5SOLAR APEX57.50x11365TOMMY BERRY C WALLER4G4.34.31210.5-100$9,668$10,68159.00.50330100381323853611278201130-Oct-2157.53-8-135.55.5
6LORD ARDMORE57.590x5711L MAGORRIAN C WALLER4G2.54.387-0.5300$5,422$6,72056.03.00010011119180080000030-Oct-2157.51-1-115.55.5
7FORTIFIED55.58166354BROCK RYAN2C WALLER4G2.24.333-4.00$9,554$9,55459.0-1.000000431233584312335800030-Oct-2153.54-3-12  
8DINADO54.05582143J R COLLETT R & M FREEDMAN5M2.93.307-1.0300$5,825$6,81255.51.000000452317392314143600030-Oct-21544-5-2322
9APACHE BELLE52.01534576MS K O'HARA W SEELIN6M1.82.5410-5.0350$3,756$5,23059.52.5014025793818424416255000130-Oct-21527-9-3800

NEURAL MSTR

3TAMPERINGJIM BYRNE8KERRY PARKER6011221132388100649100222.5$4.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
15DUFRESNETYLER SCHILLER5ANTHONY CUMMINGS54.581341055812010613200203.5$5.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
4DIFFERENT STROKESHUGH BOWMAN1KIM WAUGH59.51327141810181505622150162$7.504Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
14KOBESTARTOMMY BERRY18R & L PRICE5565762031860256140160$8.504Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
1BAROSSA ROSAJENNY DUGGAN15TRACEY BARTLEY611123152885010516160127$12.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
2DALAALAATLEE MAGORRIAN14NATHAN DOYLE6010201103010908510110124$14.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
16KISS SUMJASON COLLETT4TRACEY BARTLEY54851152815130458180119.5$17.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
11DARALINA BELLETIM CLARK6MATTHEW SMITH57122211010101205515160118$21.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
7REBEL RAMAROBBIE DOLAN2MARC CONNERS58202213381408512160110.5$26.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
5GRACE BAYJOSH PARR13CLAIRE LEVER58.51012110238805510180109$31.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
6HIGHLY DESIREDKATHY O'HARA7CLAIRE LEVER58.5122213233110557150105.5$41.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
12CONRADALYSHA COLLETT9KIM WAUGH55.513811010101004514180103$41.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
20CAESARS PALACEBRODIE LOY12ANTHONY CUMMINGS57.577715587046718090.5$61.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
8CREAM RISESNASH RAWILLER16KYLIE GAVENLOCK58851203590551115086.5$81.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
21DOM TYCOONJAY FORD17PETER ROBL56.577731583055618083$81.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
9TOO GOOD TO BE TRUANDREW ADKINS11DAMIEN LANE5875143339076815078.5$101.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
18VERBEKJEAN VAN OVERMEIRE3R & L PRICE531112233312025918078.5$101.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
17DIVINE BREATHBROCK RYAN10THERESA BATEUP53.5102153139055815075$201.004Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
10SUNBORNGLYN SCHOFIELD-EDWARD CUMMINGS57.56715107.5022514100scrscr4Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
13ALL MACHIAVELLIANJAY FORD-GREGORY HICKMAN55131712.5107.5014512160scrscr4Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
19CANYONERO -RICHARD LITT61817567.52.57.5810559160scrscr4Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good
22HEZA GENTLEMAN -M & D KEARNEY576667.52.57.508554160scrscr4Rosehill30-Oct-2114:40THE FOUR PILLARS (BM68)1500m3U BM68AUD $700,00018Good

NEURAL MSTR (races 1-3)

Horse No.HorseJockeyBarrierTrainerWeight Performance (algo)Career Performance Assessment (based on weight/class algo)Current Form (measured by class/weight algo)Revolutionary Time Assessment (adjust algo)Jockey Ability (algo)Trainer Ability (algo)Jockey/Trainer Combination (algo)Barrier Position (algo)Wet Track Performance (algo)Course Suitability (algo)Distance Suitable (algo)Prizemoney Earned (algo)Days since Last Run (algo)HCPNeural RatingPrice RatingRace No.CourseDateRace TimeRace NameDistanceClassPrize MoneyStartersTrack Conditions
1GEORGIE'S PRIDEJIM BYRNE2DESLEIGH FORSTER61322910231823140132117160214.5$3.001Rosehill30-Oct-2112:40THE AGENCY REAL ESTATE (BM78)1200m3UFM BM78AUD $130,0007Good
3SELBUROSEHUGH BOWMAN6 (7)CHRIS WALLER5916144182881105201560170.5$4.401Rosehill30-Oct-2112:40THE AGENCY REAL ESTATE (BM78)1200m3UFM BM78AUD $130,0007Good
6NEWSREADERTIM CLARK4 (5)JOHN O'SHEA56.51622210238130111022140150$5.501Rosehill30-Oct-2112:40THE AGENCY REAL ESTATE (BM78)1200m3UFM BM78AUD $130,0007Good
7BOWERY BREEZEKATHY O'HARA7 (8)P & M CAVE56.5122718385100131910160140$7.001Rosehill30-Oct-2112:40THE AGENCY REAL ESTATE (BM78)1200m3UFM BM78AUD $130,0007Good
5JUST FIELDJOSH PARR1DAMIEN LANE56.51623610331501787150122$9.001Rosehill30-Oct-2112:40THE AGENCY REAL ESTATE (BM78)1200m3UFM BM78AUD $130,0007Good
8LADY BROOKTYLER SCHILLER5 (6)JOSEPH PRIDE565125518512013148160112.5$12.001Rosehill30-Oct-2112:40THE AGENCY REAL ESTATE (BM78)1200m3UFM BM78AUD $130,0007Good
2KATALINNASH RAWILLER3JAMES CUMMINGS595112023101401731310107.5$14.001Rosehill30-Oct-2112:40THE AGENCY REAL ESTATE (BM78)1200m3UFM BM78AUD $130,0007Good
4SELEQUETOMMY BERRY-CHRIS WALLER578172027.5201313137940scrscr1Rosehill30-Oct-2112:40THE AGENCY REAL ESTATE (BM78)1200m3UFM BM78AUD $130,0007Good
9ZOROCATJASON COLLETT-DAMIEN LANE565214152.52.51014151312160scrscr1Rosehill30-Oct-2112:40THE AGENCY REAL ESTATE (BM78)1200m3UFM BM78AUD $130,0007Good
5SOLAR APEXTOMMY BERRY4 (6)CHRIS WALLER57.51117520282015082521150184.5$3.102Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0006Good
2MIGHTYBEELHUGH BOWMAN6 (8)CHRIS WALLER59.51115718288150261522180181.5$3.702Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0006Good
4MARGIE BEEJOSH PARR2KRISTEN BUCHANAN57.55131310333120112014160149$5.002Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0006Good
6LORD ARDMORELEE MAGORRIAN1CHRIS WALLER57.5241110288130121212200140$6.002Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0006Good
8DINADOJASON COLLETT3 (4)R & M FREEDMAN5441681583140121013200122$8.002Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0006Good
9APACHE BELLEKATHY O'HARA5 (7)WAYNE SEELIN5249133313044818068.5$17.002Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0006Good
1MILK MANJENNY DUGGAN-MATTHEW SMITH611121151015102913418160scrscr2Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0006Good
3PROMPT PRODIGYGLYN SCHOFIELD-EDWARD CUMMINGS595115107.51417161214130scrscr2Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0006Good
7FORTIFIEDBROCK RYAN-CHRIS WALLER55.5292014527.57.5138121321200scrscr2Rosehill30-Oct-2113:20FUJITSU GENERAL (BM78)1900m3U BM78AUD $130,0006Good
11BEAN HOTLEE MAGORRIAN4BRETT ROBB5571748103081306213150168.5$4.203Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
2ICE IN VANCOUVERTYLER SCHILLER2CODY MORGAN60462675581406820160160.5$5.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
3CASINO KIDALYSHA COLLETT10 (11)JAN BOWEN587155810881006413160154$6.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
4CEASEFIREROBBIE DOLAN3PAUL MESSARA5823225323151406619160151$7.503Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
6BLOW DARTTIM CLARK9 (10)BRETT THOMPSON56.53020110381005617160125$10.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
8TEJORITOMMY BERRY1NICK OLIVE56623201031506914180105.5$14.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
9BRAVE ENOUGHJENNY DUGGAN7CODY MORGAN567146558100611820099.5$17.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
16TESTATOR SILENSJEAN VAN OVERMEIRE11 (13)LUKE CLARKE54.561013158100662210096$20.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
5PRIVATE AGENTBRODIE LOY12 (14)KURT GOLDMAN576138153370661316095$26.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
15ALL FORMIDABLEJAMES P INNES8KEITH DRYDEN54.5549513890631020091$31.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
12CLOUD FACTORYKATHY O'HARA5STEPHEN EDWARDS5530133081208381076$41.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
7BELLASTARANDREW ADKINS6COLT PROSSER56.55153381105101016075.5$41.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
13KING'S TRUSTBROCK RYAN13 (17)TERRY ROBINSON5554553370861116072$61.003Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
1BRANDERS RULEJACK MARTIN-MITCHELL BEER6061045102.5576611140scrscr3Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
10CHEVCONIJOSH PARR-GAYNA WILLIAMS55.5612110257.51076911160scrscr3Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
14SHELBY SIXTYSIXJASON COLLETT-DANNY WILLIAMS55628152.5557799160scrscr3Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
17THE SNOOPERSTAR-MARK SCHMETZER54.54197.5307.55765610scrscr3Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good
18RAPIDITYJAY FORD-JAMIE STEWART5451272.517.57.577656100scrscr3Rosehill30-Oct-2114:00TAB HIGHWAY HCP (C3)1200m3U CL3AUD $100,00013Good

Hi, That's strange but it was only one race and today it is showing correctly for me as well. I have expanded the datasets to show 4 races. Two look to be right and two are wrong. When I import the datasets into Power BI for the 'Neurals Mstr' data you will see some horses have a "scr" in their row which means that horse is scratched from the race and not running so I filter them out of the data. These same horses show in the Race Data#1 dataset as well but I use the Neural Mstr dataset for horse name. Anyway here is a wider set of data and current output. Hopefully, you can see the issue I'm referring to but for the two races that are incorrect it looks like it is due to the horse with the lowest weight isn't showing as 0. 

donnynz_0-1635578781858.pngdonnynz_1-1635578791272.pngdonnynz_2-1635578819974.pngdonnynz_3-1635578835926.png

Due to size limits reply in a few messages sorry

But do you have a single table for RACE DATA#1? Or is it split into several tables, as in your latest pasted examples?

Regards

Hi, yes single table. I had to split it like that due to size as it was to big and wouldn't let me send the message as one, hence why i broke it up. Sorry about that.

Hi, yes single table. I had to split it like that due to size as it was to big and wouldn't let me send the message as one, hence why i broke it up. Sorry about that.

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