Loading...
Division 1 2026 odds analysis — predictability, favorite win rate & average winning odds
How the betting market actually behaves in Division 1 2026: a predictability score built from real closing prices, favorite win rate, league strength context, and the average winning odds for every market — 1X2, over/under, BTTS, draw no bet, handicaps, corners and cards — league-wide and per team.
Unlock Premium — Division 1
Unlock Premium
Instant access · Cancel anytime
Small sample — based on 12 matches. Context, not signal.
Trust & predictability
should you believe the favorites here?League predictability
UniqueLow · under 52% upset-prone — favorites are unreliable, value often hides in outsiders.
Typical · 52–57% the market is right about half the time — edges are thin either way.
High · over 57% favorites are bankable — the top 10% of leagues clear 62%.
Read: high predictability = odds-on outcomes usually land, so 1X2 is bankable — the scoreline itself can stay volatile. Zones are benchmarked against every covered league (median 54%).
League strength
context for cross-league comparisonRank groupTier 1Local
importance tier — Tier 5 is the elite group: top-5 leagues and major internationals.
Strength coef.
Avg ELO gap
Read: tier and coefficient compare quality across leagues; the ELO gap measures inequality inside this one — wide-gap leagues produce one-sided matches, so favorites land more often.
What actually wins here
the average odds of the outcome that landed, per marketPro
Period
Team
1X2
Home
Hit rate ·Avg winning odds ·Matches
Draw
Hit rate ·Avg winning odds ·Matches
Away
Hit rate ·Avg winning odds ·Matches
Double chance
1X (home or draw)
Hit rate ·Avg winning odds ·Matches
X2 (draw or away)
Hit rate ·Avg winning odds ·Matches
12 (no draw)
Hit rate ·Avg winning odds ·Matches
Handicap 0 (DNB)
Home side
Hit rate ·Avg winning odds ·Matches
Away side
Hit rate ·Avg winning odds ·Matches
Handicap 1.5
Home −1.5
Hit rate ·Avg winning odds ·Matches
Away +1.5
Hit rate ·Avg winning odds ·Matches
Goals 2.5
Over
Hit rate ·Avg winning odds ·Matches
Under
Hit rate ·Avg winning odds ·Matches
BTTS
Yes
Hit rate ·Avg winning odds ·Matches
No
Hit rate ·Avg winning odds ·Matches
Corners 9.5
Over
Hit rate ·Avg winning odds ·Matches
Under
Hit rate ·Avg winning odds ·Matches
Cards 3.5
Over
Hit rate ·Avg winning odds ·Matches
Under
Hit rate ·Avg winning odds ·Matches
| Market | Selection | Covered matches | Avg winning odds | Hit rate | Flat-bet ROI |
|---|---|---|---|---|---|
| 1X2 | Home | ||||
| Draw | |||||
| Away | |||||
| Double chance | 1X (home or draw) | ||||
| X2 (draw or away) | |||||
| 12 (no draw) | |||||
| Handicap 0 (DNB) | Home side | ||||
| Away side | |||||
| Handicap 1.5 | Home −1.5 | ||||
| Away +1.5 | |||||
| Goals 2.5 | Over | ||||
| Under | |||||
| BTTS | Yes | ||||
| No | |||||
| Corners 9.5 | Over | ||||
| Under | |||||
| Cards 3.5 | Over | ||||
| Under |
hit ratebreak-even at that price (1 ÷ odds)+%flat profit betting it every match
Read: avg winning odds = the average price of the outcomes that actually landed. Flat-bet ROI = what staking that selection in every match would have returned — the bookmaker margin drags most markets to about −5%, so anything near zero or positive stands out against that baseline. Handicap 0 = draw no bet: a draw returns your stake and is counted in the ROI. Team rows settle every match from that team's perspective — season-sized samples, so read edges as hints, not proof.
This page analyses Division 1 2026 through the bookmaker's own numbers — real closing odds settled against real results, with no model involved. The predictability panel (Pro) scores how often the market gets Division 1 right: favorite win rate, odds accuracy and an upset profile. The strength panel places the league in its global tier and measures internal inequality through the ELO gap between its strongest and weakest clubs — wide-gap leagues produce one-sided matches and short prices, tight leagues produce coin-flips. The winning-odds table (Pro) shows the average closing price of the outcome that actually settled, per market and per team, for full time and each half — the cleanest read on how winning outcomes in Division 1 have really been priced.
How predictable is Division 1? Favorite win rate and odds accuracy
The predictability panel (Pro) condenses how often the Division 1 betting market calls matches correctly. Favorite win rate is the share of matches won by the pre-match favorite — across leagues it clusters between 45% and 60%, and where Division 1 sits inside that band shows whether favorites or upsets have historically been rewarded here. Odds accuracy measures how well closing prices tracked actual outcomes across every finished match. A highly predictable league is one where favorites' records have held up; an unpredictable one is where the market leader's short price tells you far less.
League strength, tier and the ELO gap
The strength panel places Division 1 in its global importance tier — Tier 5 is the elite band of top-5 leagues and major internationals — with a strength coefficient (Pro) comparing quality across competitions. The ELO gap measures inequality inside the league: the rating distance between its strongest and weakest clubs. A wide gap means one-sided fixtures, heavy favorites and short 1X2 prices; a tight gap means genuine coin-flips deep into the table. Average home advantage (Pro) completes the picture — how many rating points playing at home is worth in Division 1, which varies far more between leagues than casual fans assume.
Average winning odds by market — what settled prices show
For every market — 1X2, over/under lines, both teams to score, draw no bet, Asian handicaps, corners and cards — the winning-odds table (Pro) averages the closing price of the selection that actually won across Division 1 2026. It answers the question a hit rate alone can't: not just how often each outcome lands, but what it paid when it did. An outcome that wins 40% of the time at average odds of 2.80 beats one winning 55% at 1.60. The team selector recomputes the table for a single club's matches, and the period toggle covers full time and both halves.
Reading market behavior from settled results
Everything on this page is derived from settled odds, not forecasts — it describes how Division 1 has actually behaved, which is the context any price needs before it can be judged. The practical loop: check predictability to decide whether favorites are trustworthy here, check the ELO gap to understand why, then use the winning-odds table to find the markets whose settled prices ran highest. Pair it with the Stats tab's over/under and BTTS rates for the same league — hit rate tells you how often, winning odds tell you at what price.
Frequently asked questions
- How predictable is Division 1 for betting?
- The predictability panel on this page (a Pro feature) scores Division 1 from real closing odds settled against real results. The two inputs worth understanding: favorite win rate — the share of matches the pre-match favorite actually won — and odds accuracy, which measures how faithfully closing prices tracked outcomes across the whole season. Leagues at the predictable end are where favorites' results have tracked their prices closely; leagues at the chaotic end are where outsiders and draws have landed far more often than the market expected. Predictability also shifts with context within a season — early rounds and end-of-season dead-rubber phases are usually the least predictable stretches — so the score should be read as the league's baseline character, not a per-match guarantee.
- How often does the favorite win in Division 1?
- The favorite win rate in the predictability panel (Pro) counts every finished Division 1 2026 match and asks whether the side with the shortest closing 1X2 price won it. Across football this number typically lands between 45% and 60% — draws are why even heavy-favorite leagues stay under two-thirds — and where Division 1 falls in that band is the single most useful fact about it as a betting market. A high rate with a wide ELO gap (see the strength panel) is structural: a few dominant clubs beat everyone. A high rate in a tight league means the market is simply well-calibrated. The upset profile alongside it shows how often longshots land, which is the raw material for underdog strategies.
- What are average winning odds and how do I read them?
- Average winning odds answer a question hit rates can't: when a given outcome in Division 1 won, what price did it close at? For each market the table (Pro) collects only the matches where that selection settled as a winner and averages their closing odds. Read it against the hit rate: a home win landing 45% of the time at average winning odds of 2.10 meant a positive realized return at those settled prices (0.45 × 2.10 > 1), while the same hit rate at 1.70 did not. Comparing selections inside one market shows where the league's returns have historically come from — for instance whether draws in Division 1 have paid enough to justify their low hit rate. It's a description of settled history, not a statement about future matches.
- How often do upsets happen in Division 1?
- Two numbers on this page answer that together. The upset side of the predictability panel (Pro) shows how often Division 1 matches are won by teams the market priced as clear outsiders — the direct measure. The ELO gap in the strength panel explains the mechanism: a league whose strongest and weakest clubs sit far apart produces structurally one-sided fixtures where upsets are rare and short prices are honest, while a compressed league produces upsets by design because 'favorite' often means a 40% chance dressed in a short price. If Division 1 shows a low favorite win rate and a tight ELO gap, outsider 1X2 selections, draw no bet on underdogs and Asian-handicap plus-lines are the markets where settled returns have historically run highest.