A team could be destined to win but somehow end up either drawing or losing altogether. That’s why the real question is this; do the big favorites win often enough to make this strategy profitable? This is an extremely difficult question to answer, partially because the term big favorite is too vague. This highlights another flaw in the strategy.

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2. Over a season how often does the favourite win the race? Hi, I have decided to build a horse racing system. I will paper bet for at. The standard favorite is probably 2 to 1. Betting at 2 to 1 breaks even with 1 correct bet in 3. And so favorites do win about 33.33% of the time. Obviously, to break even you must keep playing.
3. The beauty of English football is the exciting and popular teams. There are some teams that are expected to win a particular match drop points and the sides expected to struggle do well. This applies to Outright betting too, with clubs favourites for relegation defying the odds and challenge for promotion.

Long ago I stumbled upon an excellent paper by Hal Stern: 'On the Probability ofWinning a Football game' (1991, The American Statistician, vol. 45, no. 3, pp. 179-183).Stern took NFL pointspread data from three years in the early 1980s and then calculatedthe probability of winning conferred by the pointspread alone. For example, Stern'sdata shows that 3-point favorites win about 59% of the time. At the time of this writing, Stern's paper exists in the Google cache from a Stanford class which used it as a handout.

I made use of the paper several times, for example discussingthe odds of Ohio State getting to the BCS title game in 2007. Right after the lossto Illinois, the probability of Ohio State getting to the BCS title game was around 1%. So many teams above Ohio State lost in the subsequent weeks, that by the time I wrote that article the probability was over 50%. The probability of finishing #1 BCS (insteadof just 'top two') was only 2%, but that happened as well. It was a crazy season.

I recognized at the time that I was really abusing the numbers in Stern's paper. His numbers camefrom NFL pointspread data. NFL teams are subject to more scrutiny, and I believe that they perform on a more consistent level than college teams. I suspected that the probability of a 7-point NFL favorite losing (30%) might be different from the probability of a 7-point NCAA favorite losing.

Over the last month or two, with the generous help of Phil Steele, I've been assembling a database of all pointspreads and outcomes for BCS teams from 1999 through 2010 inclusive. The result is 9,626 games entered. (Note that each BCS-vs-BCS game is entered twice: For example, 'Kansas -2 at Kansas State, result 24-10' would also show up in the data as 'Kanas State +2 hostingKansas, result 10-24.' A game like Akron at Ohio State would only be listed once.) I divided the data into buckets by pointspread and computed the percentage of time that the favorite won in each bucket. For a 7-point favorite, that exact bucket yielded 69% wins by the favorite. Also, the best-fit curve seems likely to yield a similar value, so the NCAA numbers seem to be similar to the NFL ones.

The result, accumulatd by a simple perl script and then charted in Excel, is shown below. The Y-axisis the percent of teams which were favored by the exact amount that won their game; the X-axis is thesize of the pointspread. There's one pointspread bucket per half-point (teams with a '0' line, teamsfavored by 0.5 points teams favored by 1 point, teams favored by 1.5 points, etc.).The chart shows three plotted data sets against those axes: the full data set (red), home favorites only (green) and road favorites only (purple).

Note that some of the bumps in the higher pointspreads are exaggerated due to the small number of samples that go into those data points. For 1-point favorites through 7-point favorites, every bucket has a minimum of 200 games. But consider the purple data point around -30 which falls on the 60% line. For that bucket -- 'away games where the visiting team is a 30.5-point favorite' -- there are only three data points (two BCS-vs-BCS games each counted twice, and one other). Rutgers in 1999 beat Syracuse despite being 30.5-point underdogs, which (as a double-counted game, yielding only 3/5 wins by thefavorite) by itself accounts for that deviation.

That's also true for the overly-high purple data point on the extreme left of the graph: there was only one 0.5-point road favorite in my data set. That team happened to win the game, to yield 100% winners for that bucket. With college overtime, it's not possible for a game to end in a tie score. As a result spreads of +0.5, 0, and -0.5 are all effectively the same.

The raw data that goes into the chart, is:

Spread	Favorite Wins	Games	Win Pct	Home Fav wins	Home Games	Home Win %	Away Fav wins	Away Games	Away Win %
0	25	46	54.35	8	17	47.06	11	18	61.11
0.5	2	4	50.00	0	2	0.00	2	2	100.00
1	125	243	51.44	52	99	52.53	48	99	48.48
1.5	95	211	45.02	44	89	49.44	42	100	42.00
2	132	206	64.08	51	93	54.84	64	83	77.11
2.5	179	396	45.20	85	192	44.27	75	160	46.88
3	328	587	55.88	143	263	54.37	154	264	58.33
3.5	293	483	60.66	124	208	59.62	129	208	62.02
4	156	241	64.73	76	121	62.81	58	88	65.91
4.5	145	241	60.17	78	125	62.40	51	88	57.95
5	115	173	66.47	61	95	64.21	39	60	65.00
5.5	124	206	60.19	71	104	68.27	41	82	50.00
6	178	276	64.49	93	142	65.49	70	106	66.04
6.5	266	372	71.51	158	215	73.49	87	118	73.73
7	299	433	69.05	151	211	71.56	128	182	70.33
7.5	243	328	74.09	143	183	78.14	81	110	73.64
8	126	186	67.74	67	104	64.42	41	61	67.21
8.5	129	179	72.07	75	110	68.18	42	57	73.68
9	104	142	73.24	58	75	77.33	30	46	65.22
9.5	115	162	70.99	73	96	76.04	36	57	63.16
10	190	248	76.61	105	141	74.47	67	83	80.72
10.5	116	155	74.84	69	94	73.40	36	48	75.00
11	102	141	72.34	64	92	69.57	36	43	83.72
11.5	98	124	79.03	52	71	73.24	43	48	89.58
12	105	124	84.68	61	72	84.72	34	40	85.00
12.5	105	123	85.37	78	93	83.87	24	26	92.31
13	111	147	75.51	68	92	73.91	38	49	77.55
13.5	206	253	81.42	143	174	82.18	60	76	78.95
14	182	220	82.73	121	146	82.88	47	56	83.93
14.5	126	148	85.14	83	95	87.37	35	43	81.40
15	95	106	89.62	67	74	90.54	25	29	86.21
15.5	82	96	85.42	56	65	86.15	26	29	89.66
16	77	97	79.38	49	60	81.67	28	37	75.68
16.5	109	129	84.50	71	88	80.68	35	38	92.11
17	118	130	90.77	86	94	91.49	30	34	88.24
17.5	111	125	88.80	80	90	88.89	29	33	87.88
18	72	77	93.51	52	57	91.23	20	20	100.00
18.5	71	76	93.42	51	56	91.07	18	18	100.00
19	72	81	88.89	45	52	86.54	25	27	92.59
19.5	58	69	84.06	46	51	90.20	12	18	66.67
20	100	106	94.34	72	78	92.31	27	27	100.00
20.5	74	80	92.50	52	56	92.86	19	21	90.48
21	139	141	98.58	101	103	98.06	36	36	100.00
21.5	74	78	94.87	56	60	93.33	18	18	100.00
22	71	78	91.03	49	54	90.74	18	20	90.00
22.5	44	47	93.62	31	33	93.94	13	13	100.00
23	61	65	93.85	39	39	100.00	22	26	84.62
23.5	61	64	95.31	49	50	98.00	12	14	85.71
24	69	73	94.52	53	57	92.98	16	16	100.00
24.5	72	78	92.31	55	58	94.83	14	17	82.35
25	45	50	90.00	30	31	96.77	15	19	78.95
25.5	38	39	97.44	35	36	97.22	2	2	100.00
26	36	38	94.74	29	31	93.55	6	6	100.00
26.5	40	40	100.00	31	31	100.00	7	7	100.00
27	65	70	92.86	51	54	94.44	14	16	87.50
27.5	49	49	100.00	40	40	100.00	6	6	100.00
28	62	62	100.00	46	46	100.00	11	11	100.00
28.5	31	32	96.88	28	29	96.55	3	3	100.00
29	34	37	91.89	28	31	90.32	4	4	100.00
29.5	34	35	97.14	30	31	96.77	3	3	100.00
30	51	51	100.00	34	34	100.00	17	17	100.00
30.5	33	35	94.29	28	28	100.00	3	5	60.00
31	30	31	96.77	22	23	95.65	7	7	100.00
31.5	22	22	100.00	20	20	100.00	2	2	100.00
32	34	34	100.00	28	28	100.00	6	6	100.00
32.5	18	18	100.00	16	16	100.00	2	2	100.00
33	33	33	100.00	26	26	100.00	7	7	100.00
33.5	17	17	100.00	17	17	100.00	-	-	-
34	25	25	100.00	24	24	100.00	1	1	100.00
34.5	21	21	100.00	21	21	100.00	-	-	-
35	31	32	96.88	31	32	96.88	-	-	-
35.5	25	25	100.00	23	23	100.00	2	2	100.00
36	20	20	100.00	19	19	100.00	1	1	100.00
36.5	15	15	100.00	13	13	100.00	2	2	100.00
37	13	15	86.67	10	12	83.33	3	3	100.00
37.5	11	11	100.00	8	8	100.00	3	3	100.00
38	17	17	100.00	15	15	100.00	2	2	100.00
38.5	4	4	100.00	4	4	100.00	-	-	-
39	10	10	100.00	6	6	100.00	4	4	100.00
39.5	7	7	100.00	7	7	100.00	-	-	-
40	8	8	100.00	6	6	100.00	2	2	100.00
40.5	12	12	100.00	11	11	100.00	1	1	100.00
41	21	23	91.30	18	20	90.00	3	3	100.00
41.5	9	9	100.00	8	8	100.00	1	1	100.00
42	12	12	100.00	12	12	100.00	-	-	-
42.5	6	6	100.00	4	4	100.00	2	2	100.00
43	4	4	100.00	4	4	100.00	-	-	-
43.5	6	6	100.00	6	6	100.00	-	-	-
44	6	6	100.00	6	6	100.00	-	-	-
44.5	5	5	100.00	4	4	100.00	-	-	-
45	10	10	100.00	10	10	100.00	-	-	-
45.5	4	4	100.00	4	4	100.00	-	-	-
46	3	3	100.00	3	3	100.00	-	-	-
46.5	3	3	100.00	3	3	100.00	-	-	-
47	4	4	100.00	4	4	100.00	-	-	-
47.5	1	1	100.00	1	1	100.00	-	-	-
48	4	4	100.00	2	2	100.00	2	2	100.00
48.5	1	1	100.00	1	1	100.00	-	-	-
49	4	4	100.00	4	4	100.00	-	-	-
49.5	2	2	100.00	2	2	100.00	-	-	-
50	2	2	100.00	2	2	100.00	-	-	-
51	2	2	100.00	2	2	100.00	-	-	-
51.5	1	1	100.00	1	1	100.00	-	-	-
52	1	1	100.00	1	1	100.00	-	-	-
53	2	2	100.00	2	2	100.00	-	-	-
56	1	1	100.00	1	1	100.00	-	-	-
59.5	1	1	100.00	1	1	100.00	-	-	-

For those interested in the raw data (all the individual game results), I hope to make it available soon.For now, you can clickhere to download an Excel spreadsheet containing the chart above and the collated data that goes into it. Also, I will work on further analysis on the data, coming up with a best-fit curve and a conversion chart for pointspread to win-percentage for that.

A quick search for betting on favourites on Google throws up hundreds of “fool-proof” systems for making money, which simply require your credit card details and a one-time payment of $100 to read about them. And one of the most common “systems” you will encounter is betting on favourites, because here we find there is some truth behind the lies.

A lot of academic research has been done into the so-called favourite longshot bias in betting markets. All winning betting strategies are based on exploiting market inefficiencies and when it comes to favourites there appears to be rather a large one in place. Put simply, favourites are much closer to “true odds” than longshots.

One major study you will find quoted in lightly researched articles on favourite betting is by two American academics who examined the results of over 6 million horse races in America and found backing favourites lost at a rate of 5.5% while backing from 3/1-15/1 lost at 18% on average.

The good news for bookmakers there is no matter what people bet on they lose, but it’s a significant difference between favourites and longer odds bets. Favourites are priced up more accurately than longshots. It’s also been shown to apply to other markets with particular application to political and novelty markets where it seems the shorter the odds the greater the bias.

Favourites in Football

In the last 20 years a number of research papers have been published to see if this applies to football with confusingly mixed results. Some say yes, some say no and some say both. If you were hoping that a simple “always bet on the favourite” strategy was your route to profit, then think again. But there are some key lessons:

1. Betting on the favourite is rarely a bad bet
2. Shorter priced favourites are often better value than longer priced ones
3. You need to do some work of your own

What various academic and recreational research from bettors has found is betting on favourites generally allows you to lose more slowly. This isn’t a great long-term strategy, but as a starting point it at least demonstrates that betting the favourite is rarely a bad bet. As a starting point, losing at a slow rate is a damn sight better than most punters manage.

For some bettors the nature of betting short-priced favourites seems counterintuitive to their notion of “value”. Risk reward is an odd concept, and betting £100 to win £20 on a 1.2 selection doesn’t seem hugely attractive to many, but research has shown this is often a better bet than £100 on a 1.8 shot in terms of expected long-term returns.

A good demonstration of this comes in rugby union, where New Zealand will often be priced at something absurdly unattractive like 1/80 to beat most sides outside the top six. But losses to those teams are extremely rare. In fact the All Blacks have never lost to an international team that isn’t Australia, England, South Africa, France or Wales. Never. How good does your 1/80 look now?

The All Blacks are perennial favourites. Their 2015 World Cup winning team is regarded by many as the greatest rugby team ever assembled

When favourites betting goes bad

But if you are too cavalier with these kinds of stats you can come unstuck as South Africa showed in the Rugby World Cup where they were 1/100 to beat Japan. The Springboks had similar stats to New Zealand, having only lost internationals to 8 teams in their history. The loss to the minnows of Japan was the biggest shock of the tournament, but it should be seen as an exception and not the rule.

There were warning signs including an improving Japanese side a South African side that lost all three of its games in the Rugby Championship including at home to Argentina. And the final and most important point is you can’t just trust blindly in backing short-priced favourites and expect to never be stung for a big loss now and again.

Take the 2015/16 Premier League as an example of how favourites perform on a long-term betting basis. The 2014/15 season threw up a rather conveniently even 100 wins from 150 games where the home side was under 2.0. In other words odds-on to win. This would have given a total profit of £4.85 to a £1 level stakes bet. Pretty good.

The following season, by early February, there were 50 winners from 91 games for a total loss of £13.75 to the same level £1 stakes. The 2013/14 season had 99 winners from 143 games for a season-long profit of £1.21. So it shows that an expected long-term trend can sometimes go wildly off track.

In Conclusion

So what does all this tell us? Well firstly that this is not an exact science and secondly that despite the 15/16 anomaly there is a lot of value to be had in odds on favourites. What it should encourage you to do is go and analyse the huge wealth of stats and betting data that exists for free on the internet and try and find your own conclusions.

Find a data source and play around with the results. Spot a pattern and develop a system that works for you by refining the data and using your own insight into what might make odds-on chances more or less likely to win. But don’t, whatever you do, fall into the trap of thinking a 1.20 bet doesn’t present value.

How Often Do The Favorites Win In Football

Top Tips

How Often Do Nfl Favorites Win

• Betting on the favourite is rarely a bad bet
• Shorter priced favourites are often better value than longer priced ones
• You need to do some work of your own