Sharpen your pregame football bets using betting data movements

» Betting strategies

Sooner or later in every bettor's life, there is a time when questions like "Should I purchase betting picks" or "Should I follow that tipster" will need an answer. If the answer is yes, probably you are trying to follow a skilled tipster with genuine track record credibility. If you will bet blind, you should have almost the same track profit record as the tipster itself.

Is it possible that we could sharpen the original tipster's picks record ?

Sport betting case study: How to improve the betting service provider's performance through pregame betting data movements and patterns ?

At the time of writing, we did a deep scan of the pregame odds movements for the last 25 football (soccer) picks from the www.bet-ibc/picks (from # 68 to # 20 ) where all tipsters seems to have good reputation and positive results in long run.

In our research we skip 3 football matches as follow:
1.Levante vs. Real Madrid (3 Feb. 2018 - pick Over 1.5 first half @ 1.92). Reason: we are not monitoring yet betting data movements only for first half.
2.Accrington Stanley vs. Morecambe (1 Jan. 2018 - pick Over 2.5 @ 1.98). Reason: no final result because the match was postponed at intermission (heavy rain).
3.AS Roma vs. Sassuolo (30 Dec. 2017 - pick Roma total goals Under 2.25 @ 2.04). Reason: we are not we are not monitoring yet betting data movements for every participant alone.

What we did ?
For each tip, we monitor the odds movements for the last 24h before starting time of the event. If the odds for that 1X2 moneyline prognostication (let's say "α" odd) is lowering for the last 24h period of time, before start of the event and could be made a virtual arbitrage between "α"(@24h before starting time) and "β"(@ starting time) with "δ"(@ starting time) then we have an "approval" for betting. If the "α" odd is not lowering enough for any virtual arb then we have a "denial" for betting and we'll skip that tip. The same pattern odds movements is searched for +AH/-AH or Over/Under.

Conclusion
1. If we had bets with 1 unit flat stake on each tip (betting all tips), we would have 2.357 units in profit from all 25 football matches.
(24 bet tickets; 12 wins; 12 losses; average odd = 2.357)
2. If we had bets with 1 unit flat stake only on tips which pass the above filter, we would have 6.135 units in profit from 8 football matches.
(8 bet tickets; 7 wins; 1 loss; average odd = 2.013).

Details of all the above events with graphs and explanations can be found here.
A powerful conclusion could be made after a deep research with at least hundreds of events.

Contact us if you'll find other proofs of betting strategies which involves odds data movements.