Salient Markets And Convex Bets

Our WCMI metric captures how much the crowd is 'in agreement' in any particular race but does not tell us what fundamental factors it considers. We should view the betting market as a second-order 'emergent phenomenon and not a first-order algorithm-based calculation as the crowd is not collaborating in real-time.

Does the crowd use a single, coherent set of fundamental factors in selecting favorites?

Using an entropy-based measure of feature importance, we can identify which publicly-available information strongly correlates with the betting market order. Though the process is error-prone (in particular, we can never be sure that we have identified all the relevant factors), it may guide us (weekend warriors) in identifying 'live longshots' (convex bets) using dimensions that the crowd is discounting.

Convex Bets: Place bets on 'live longshots' leading to many small losses and occasionally punctuated by a few large wins!

On a typical racecard, there are two types of publicly-available information with which the crowd can make its deliberations - for example:

• Simple [Back Of Envelope]: (Age, Days Since Run, [Barrier|Draw|Gate|Stall], Form Figures, Gender, Previous Runs, Weight Carried); and
• Complex [Proprietary]: (Official Rating, Pedigree, Speed Rating, Strength Of Opposition, [Barn|Handler|Stable|Trainer] Form).

Concerning low-quality handicap racing on all-weather surfaces (2150 races) and considering only the simple factors, 'weight carried' tends to have the highest impact on the betting market (roughly three times its natural weighting - (1 / factors)). Comparatively, less impact is given to both 'horse age' and '[Barrier|Draw|Gate|Stall]' with the remaining factors having little effect. Of the complex factors, '[Barn|Handler|Stable|Trainer] form' has the highest impact, followed by 'strength of opposition' faced in prior races.

Though we are not claiming cause and effect, we can still make the plausible inference that we must try to identify our convex bets using dimensions that the crowd is discounting - for example, FPR. It is a losing proposition (long-term) to go head-to-head with the crowd on its territory! Therefore, we could focus our attention - for example - on unexposed (low 'previous runs') runners with good pedigrees and improving FPRs to identify 'convex bets'.

Considering only low-quality handicap racing on all-weather surfaces, it is surprising that 'weight carried' significantly impacts the betting market. This result is unexpected, given that the rationale for handicap racing is to equate horses' chances of winning by allotting weights according to past performances..

In sum, whatever sport you specialize in handicapping, you should carefully examine (not guess) what publicly-available factors the crowd is using to create the various betting markets!

CsvPredictor: Turns Historical Record Into Mini Prediction System

CsvPredictor turns a historical record in CSV format into a mini prediction system. The program is completely agnostic with respect to the domain knowledge captured in the file (e.g. weather conditions, successful movies, past performances). 

Running CsvPredictor.exe with a valid csv file will result in a QnA session based on the salience of the features (columns), effectively, turning a standard flat file into a data mining classification tree. 
For example, whether or not to play ball given current weather conditions:

    C:\CsvPredictor>CsvPredictor.exe PlayBall.csv
    CsvPredictor v2.41
    Input File: "PlayBall.csv" (14 records and 4 features)
    Top Features (Salience)
    Outlook   0.46176
    Humidity  0.36618
    Wind      0.11693
    Q. Is Outlook  =  ["Overcast"; "Rainy"; "Sunny"]?  Sunny
    Q. Is Humidity =  ["High"; "Normal"]?  Normal
    A. Predict: PlayBall = True
    C:\CsvPredictor>

or checking the likelihood of a new movie being a blockbuster!

    C:\CsvPredictor>CsvPredictor.exe Movies.csv
    CsvPredictor v2.41
    Input File: "Movies.csv" (2690 records and 5 features)
    Top Features (Salience)
    Budget              0.34871
    Genre               0.26719
    Production Country  0.24084
    Runtime             0.11430
    Q. Is Budget =  ["<=15000000.00"; "<=44263333.33"; "<=380000000.00"]? 
                    <=15000000.00
    Q. Is Genre =  ["Action"; "Adventure"; "Animation"; "Comedy"; "Crime"; 
                    "Documentary"; "Drama"; "Family"; "Fantasy"; "Foreign"; 
                    "History"; "Horror"; "Music"; "Mystery"; "Romance"; 
                    "Science Fiction"; "Thriller"; "War"; "Western"]?  
                    Action
    Q. Is Production Country =  ["Australia"; "Canada"; "Hong Kong"; 
                                 "Ireland"; "United Kingdom";
                                 "United States of America"]?  
                                 United States of America
    Q. Is Runtime =  ["<=99.47"; "<=115.00"; "<=248.00"]?  <=115.00
    Q. Is Release Month =  ["<=5.00"; "<=9.00"; "<=12.00"]?  <=12.00
    A. Predict: Success = True
    C:\CsvPredictor>

Note, it is very important to state that this program is only intended to provide an easy entry-point to data analytics for handicappers and is, in no way, intended to replace the advice and expertise of professional data analysts and statisticians!

ExMachina Handicapping Rules (Excel Add-In)

Many of us spend countless hours trawling through historical records in a vain attempt to gain new insights into the key fundamental factors that will enhance our sports handicapping. Unfortunately, our innate cognitive biases (e.g. anchoring, availability, confirmation) continually invade all attempts at a quasi-scientific approach to data mining. Ideally, we would like a quick-fix solution to this dilemma – no new learning required and automatically works with available tools!

To that end, enter the ExMachina Excel Add-In (32-bit and 64-bit), which takes as input a CSV file of historical data and outputs a set of decision rules. In brief, the goal is to identify the most salient attributes in the data file and to create a set of rules based on that specific subset. Note, it is very important to state that this Excel Add-In is only intended to provide an easy entry-point to data analytics for handicappers and is, in no way, intended to replace the advice and expertise of professional data analysts and statisticians.

If you are interested in reviewing how the Excel Add-In works, then download the following MP4 file – ExMachina Handicapping Rules.