I would like to present the results of a long-term research project focused on pre-match football betting markets and discuss the possibility of professional cooperation.
The model was developed on the Orbit Exchange platform and is not based on the traditional approach of seeking an edge through expected odds movement after a bet is placed.
After several years of research, the project identified pre-match markets where current market prices do not fully reflect the underlying value.
In other words, the research revealed a hidden market inefficiency - situations where the market price does not accurately represent the true probability of the outcome.
One of the most important findings is that this inefficiency is generally not corrected through normal market movement before kick-off. In many cases, odds remain almost unchanged, and sometimes they even move in the opposite direction, increasing shortly before the match starts.
The research was conducted over four consecutive football seasons.
During this period, approximately 4,000 football matches were studied in detail, together with historical market data and the behaviour of exchange odds.
The model applies exclusively to pre-match football markets and is based on analysis across more than 50 national leagues and competitions worldwide.
The research includes major European, Asian, Australian and Latin American football markets, including the English Premier League, Championship, League One, League Two, Bundesliga, Serie A, La Liga, Ligue 1, as well as other highly liquid first and selected second divisions.
Women’s football, youth competitions, friendly matches and low-liquidity tournaments were intentionally excluded.
The latest verified sample from the 2025/2026 season produced the following results:
Over 2.5 Goals
916 matches;
average best available odds on Orbit Exchange: approximately 2.20;
net ROI after Orbit Exchange commission (3%): approximately 9-10%.
The same sample was also tested on additional related markets.
Both Teams To Score (BTTS)
916 matches;
average best available odds on Orbit Exchange: approximately 1.99;
net ROI after commission: approximately 7-8%.
Over 0.5 First Half Goals
916 matches;
average best available odds on Orbit Exchange: approximately 1.49;
net ROI after commission: approximately 4%.
For me, one of the most important aspects is that the positive mathematical expectation is demonstrated not only on a single market, but across several related pre-match markets within the same match sample.
In my opinion, this provides additional evidence that the model identifies match characteristics which are systematically undervalued by current market prices.
After several years of detailed research and practical application, I hope to find an ally with whom we can scale the full potential of this project.
My goal is to find an ally with the necessary infrastructure, technology, capital and operational capabilities to fully realize this potential.
If these results and the underlying model are of interest, I believe we can find an appropriate structure for cooperation - naturally, one based on mutual trust and protection of both parties’ interests
P.S. The model challenges the concept that positive ROI is only possible by having an edge over the closing line, unless the factor of a hidden edge is taken into account.
The model was developed on the Orbit Exchange platform and is not based on the traditional approach of seeking an edge through expected odds movement after a bet is placed.
After several years of research, the project identified pre-match markets where current market prices do not fully reflect the underlying value.
In other words, the research revealed a hidden market inefficiency - situations where the market price does not accurately represent the true probability of the outcome.
One of the most important findings is that this inefficiency is generally not corrected through normal market movement before kick-off. In many cases, odds remain almost unchanged, and sometimes they even move in the opposite direction, increasing shortly before the match starts.
The research was conducted over four consecutive football seasons.
During this period, approximately 4,000 football matches were studied in detail, together with historical market data and the behaviour of exchange odds.
The model applies exclusively to pre-match football markets and is based on analysis across more than 50 national leagues and competitions worldwide.
The research includes major European, Asian, Australian and Latin American football markets, including the English Premier League, Championship, League One, League Two, Bundesliga, Serie A, La Liga, Ligue 1, as well as other highly liquid first and selected second divisions.
Women’s football, youth competitions, friendly matches and low-liquidity tournaments were intentionally excluded.
The latest verified sample from the 2025/2026 season produced the following results:
Over 2.5 Goals
916 matches;
average best available odds on Orbit Exchange: approximately 2.20;
net ROI after Orbit Exchange commission (3%): approximately 9-10%.
The same sample was also tested on additional related markets.
Both Teams To Score (BTTS)
916 matches;
average best available odds on Orbit Exchange: approximately 1.99;
net ROI after commission: approximately 7-8%.
Over 0.5 First Half Goals
916 matches;
average best available odds on Orbit Exchange: approximately 1.49;
net ROI after commission: approximately 4%.
For me, one of the most important aspects is that the positive mathematical expectation is demonstrated not only on a single market, but across several related pre-match markets within the same match sample.
In my opinion, this provides additional evidence that the model identifies match characteristics which are systematically undervalued by current market prices.
After several years of detailed research and practical application, I hope to find an ally with whom we can scale the full potential of this project.
My goal is to find an ally with the necessary infrastructure, technology, capital and operational capabilities to fully realize this potential.
If these results and the underlying model are of interest, I believe we can find an appropriate structure for cooperation - naturally, one based on mutual trust and protection of both parties’ interests
P.S. The model challenges the concept that positive ROI is only possible by having an edge over the closing line, unless the factor of a hidden edge is taken into account.
