At this point it might be worth mentioning a bit about what I am tracking here and how it is being tracked. I am obviously looking at whether its possible to detect more or less positive selections from Hugh Taylor, but what measures are being used.

I am tracking at the moment on the following criteria which I will then try and explain.

Profit on Positive Probability
Profit on Polarity
Profit on Subjectivity
Profit on tip text length
Profit on good old back them all to BFSP

All the above are to BFSP before commission.

OK so what do they mean ?. The last two are self explanatory, the next to last being simply is he more confident when he has more to say ?.

To explain the first three I need to now mention the Python library being used to carry out the analysis. The library is called TextBlob. There are a plethora of introductions out there should you be interested in the detail and it is by no means the only option for this kind of work.

To analyze and gain sentiment analysis on any text the library has to refer to a Lexicon of words, phrases, sentences if it is to determine what is positive and what is negative. So the word ‘great’ in some text may push the overall positive probability up whilst the word ‘poor’ or even ‘not great’ will pull it down.

TextBlob comes with two ready to use corpuse’s to refer to. One is a library of movie reviews and it is this option that gives us the positive probability scores (lets hope Hugh does not tip anything called The Shawshank Redemption). The other is based on lexicon of words and similar positivity scores and gives us the Polarity score, a measure of positivity, and the Subjectivity score, a measure of how subjective or objective the text is. At this stage I am not sure that the Subjectivity score will be very useful but lets track it and see.

Another option, once a back catalogue of Hughs text builds up, is to train an algorithm on Hugh’s language itself so that future selections are assessed based on his past language and their success rate. This is why I am logging not just winners but placed horses too as his win rate is low and this may prove a problem when training such an algorithm. That is for the back burner for now, let’s continue to see if how people feel about their movies is how Hugh feels about his horses (Godfather excluded).

I will update at the end of week 1 with the figures on this blog entry.

End of week 1

If you had backed all Hughs tips to BFSP you would be -4.02 pts down
If you had backed or layed them as indicated by positivity probability you would be -0.02 pts down
If you had back or layed depending on text length you would have been -7.98 pts down
If you had backed or layed as indicated by sentiment polarity you would be -1.98 pts down
If you had layed where Hugh appears more subjective and backed where he appears less subjective you would be +5.98 pts up

Back all Plays/Back all PL
10/-4.02
Avg’ Prob’ Plays/Avg Prob PL
10/-0.02
Text Length Plays/Text Length PL
10/-7.98
Avg Polarity Plays/Avg Polarity PL
10/-1.98
Avg Subjectivity Plays/Avg Subjectivity PL
10/5.98

End of week 2 18/3/18

Back all Plays/Back all PL
20/-4.27
Avg’ Prob’ Plays/Avg Prob PL
20/+1.73
Text Length Plays/Text Length PL
20/+3.77
Avg Polarity Plays/Avg Polarity PL
20/-3.77
Avg Subjectivity Plays/Avg Subjectivity PL
20/-5.77

End of week 3 25/3/2018

Back all Plays/Back all PL
31/-7.92
Avg’ Prob’ Plays/Avg Prob PL
31/+6.08
Text Length Plays/Text Length PL
31/-0.58
Avg Polarity Plays/Avg Polarity PL
31/-10.12
Avg Subjectivity Plays/Avg Subjectivity PL
31/+12.12

End of week 4 1/4/2018

Back all Plays/Back all PL
39/-3.78
Avg’ Prob’ Plays/Avg Prob PL
39/-18.06
Text Length Plays/Text Length PL
39/-12.72
Avg Polarity Plays/Avg Polarity PL
39/2.02
Avg Subjectivity Plays/Avg Subjectivity PL
39/+22.26

End of week 5 8/4/2018

Back all Plays/Back all PL
50/+1.74
Avg’ Prob’ Plays/Avg Prob PL
50/-28.14
Text Length Plays/Text Length PL
50/-16.8
Avg Polarity Plays/Avg Polarity PL
50/-6.7
Avg Subjectivity Plays/Avg Subjectivity PL
50/+15.38

End of Week 6 15/4/2018
Back all Plays/Back all PL
62/+10.69
Avg’ Prob’ Plays/Avg Prob PL
62/-17.19
Text Length Plays/Text Length PL
62/-14.85
Avg Polarity Plays/Avg Polarity PL
62/-25.65
Avg Subjectivity Plays/Avg Subjectivity PL
62/+4.93

End of Week 7 22/4/2018
Back all Plays/Back all PL
74/+30.61
Avg’ Prob’ Plays/Avg Prob PL
74/+4.73
Text Length Plays/Text Length PL
74/-0.93
Avg Polarity Plays/Avg Polarity PL
74/-46.37
Avg Subjectivity Plays/Avg Subjectivity PL
74/-14.19

End of week 8 29/4/2018
Back all Plays/Back all PL
87/+43.15
Avg’ Prob’ Plays/Avg Prob PL
87/-8.49
Text Length Plays/Text Length PL
87/+7.15
Avg Polarity Plays/Avg Polarity PL
87/-21.83
Avg Subjectivity Plays/Avg Subjectivity PL
87/-38.73

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