Does technical analysis for sports betting work

The global Online Sports Betting market size was valued at USD million in and is expected to expand at a CAGR of % during the. Secondly, their technology can give betting operators an advantage over competitors. Thirdly, sports analytics does technical analysis for sports betting work bookies minimize risk and. With today's technology, the pricing of stocks is updated within a few milliseconds of real-time. This is way faster than a human is able to. Traders must carefully determine their position size and set stop-loss orders to limit potential losses. While the Forex market can be volatile, strategic risk.

Does Technical Analysis for Sports Betting Work?

In the world of sports betting, enthusiasts are always on the lookout for ways to gain a competitive edge. Various strategies and methodologies are employed, with one of the most prevalent being technical analysis. But the burning question remains - does technical analysis truly work when it comes to improving your chances in sports betting?

Many sports bettors swear by technical analysis, claiming it helps them make more informed decisions and predict outcomes with greater accuracy. The process involves studying historical data, trends, and statistical patterns to forecast future results. It is akin to the analysis used in financial markets, with a focus on numbers and statistical probabilities.

Proponents argue that technical analysis can provide valuable insights into team performance, player form, and other critical factors that affect the outcome of sporting events. By identifying patterns and trends, bettors believe they can make smarter bets and increase their chances of winning.

However, critics of technical analysis in sports betting dismiss it as nothing more than a pseudoscience. They argue that the inherent unpredictability of sports makes it impossible to rely solely on historical data and statistical analysis. Factors such as player injuries, team dynamics, and external variables can all play a significant role in determining the outcome of a game.

Furthermore, the presence of randomness and unpredictability in sports can often render technical analysis ineffective. A winning streak or an upset victory may defy all statistical predictions, highlighting the limitation of solely relying on historical data.

While technical analysis can provide valuable insights and serve as a useful tool in sports betting, it is essential to approach it with caution. Combining technical analysis with other forms of analysis, such as fundamental analysis and expert opinions, may offer a more comprehensive approach to making informed betting decisions.

In conclusion, while technical analysis can offer valuable insights and help bettors make more informed decisions, it is not a foolproof strategy. Success in sports betting often requires a combination of different approaches and a nuanced understanding of the complexities of the sporting world.

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No wonder the patterns work so well, if traders are forcing the markets in this way. That said, perhaps the idea deserves to be answered in more depth and possibly refuted. For many years traders have studied chart patterns, and the main patterns which indicate favorable trades are certainly well known. However, to actually discern and identify any particular pattern requires some skill and interpretation, and because there can potentially be infinite variation in a stock chart there is always some question about which if any pattern is being exhibited.

It is for this reason that many people assert that trading is as much an art as a science. This means that not all traders will consider the opportunity for a trade in the same way. Consider, if everyone interpreted the market moves in a single unambiguous way, then everyone would be making a fortune.

Even amongst the traders who decided that the pattern was a tradable one, they would each bring their own interpretation to the opportunity. For instance, some would try to anticipate a potential reversal and trade early, while others would look for a definite move and then trade in the established direction of the trend.

As for trading being as much an art as a science, certainly there is a learning curve beyond scientific and mathematical analysis. This course will cut through the time needed for that by providing you with the lessons learned from experience. Even if there was a self fulfilling prophecy aspect to trading, any harm it did would be self-correcting.

The shares soon fall back to their true value. This tactic works to a limited extent, but only because the selected shares had hardly any buying and selling before, so any activity made the price overreact. To think that traders, acting on their own and without a specific stimulus like this, would be able to create a market move in any major stock is probably attributing too much power to the trading community.

This entry is filed under course. You can follow any responses to this entry through the RSS 2. You can leave a response , or trackback from your own site. Name required. Mail will not be published required. Does technical analysis for sports betting work Please contact us if you wish to reproduce any of it. This is expected to translate to improved generalization to future data. The three types of wagers considered in this work—point spread, moneyline, and over-under—are the most popular bet types in North American sports.

One unique aspect of American football is its scoring system, in which the points accumulated by each team increase primarily in increments of 3 or 7 points. The structure of the scoring imposes constraints on the distribution of the margin of victory m. In the case of games in the National Basketball Association NBA , the most common margins of victory tend to occur in the interval, reflecting the overall higher point totals in basketball and its most common point increments 2 and 3.

In this fictitious example, the median is 7 but the mean is Assuming that one has committed to wagering on the match, the optimal decision is to bet on the visiting team, despite that fact that the home team has won the previous matches by an average of 15 points. The figures and tables in this manuscript may be reproduced by executing the notebook.

The latter was utilized for all analysis. In order to estimate quantiles of the distributions of margin of victory and point totals from heterogeneous data i. This permitted the estimation of the 0. Only spreads or totals with at least matches in the dataset were included, such that estimation of the median would be sufficiently reliable. It is likely that the resulting error is negligible, however, due to the likelihood of the payout discrepancy being fairly balanced across the home and visiting teams.

In order to overcome the discrete nature of the margins of victory and point totals, kernel density estimation was employed to produce continuous quantile estimates. The KernelDensity function from the scikit-learn software library was employed with a Gaussian kernel and a bandwidth parameter of 2. For the margin of victory, the density was estimated over points ranging from to For the analysis of point totals, the density was estimated over points ranging from 10 to The regression analysis relating median outcome to sportsbook estimates Fig 1 was performed with ordinary least squares OLS.

In order to generate variability estimates for the 0. The confidence intervals were then constructed as the interval between the 2. To quantify the relationship between a sportsbook bias and the associated upper bound on wagering performance, the empirical CDF of each stratified sample was converted into an expected profit, conditioned on a hypothetical spread or total that deviated from the true median by fixed increments of -3, -2, -1, 0, 1, 2, and 3 points.

More specifically, the expected values were first computed separately for the case of wagering on the home and visiting teams:. To model the idealized case of always placing the wager on the side with the higher probability of winning against the spread, the reported expected profit was taken as the maximum of the two expected values in The analogous procedure was conducted for the analysis of point totals.

The author would like to thank Ed Miller and Mark Broadie for fruitful discussions during the preparation of the manuscript. The author would also like to acknowledge the effort of the reviewers, in particular Fabian Wunderlich, for providing many helpful comments and critiques throughout peer review. After careful consideration, we have decided that your manuscript does not meet our criteria for publication and must therefore be rejected.

I am sorry that we cannot be more positive on this occasion, but hope that you appreciate the reasons for this decision. I have now heard back from an expert reviewer on your paper titled "Statistical Principles of Optimal Decision-Making in Sports Wagering. The reviewer provided comments and suggestions which I believe will be valuable in improving your work and potentially finding a more suitable outlet for publication.

I regret to inform you of this disappointing decision. I wish you the best of luck with your research and future publications. The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes.

The conclusions must be drawn appropriately based on the data presented. The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception please refer to the Data Availability Statement in the manuscript PDF file. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository.

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Please upload your review as an attachment if it exceeds 20, characters. This paper analyzes the efficiency of betting markets for American professional football games from The analysis includes bets on the margin of victory point spread and on the total number of points scored. In each case, bets are analyzed that are approximately even money bets.

The most common arrangement is that a bettor wins the amount bet if they win but loses 1. The analysis is conducted non-parametrically. Games are placed in bins according to their point spread and again according to their point totals. Assuming that a bin ends up containing games or more, the paper calculates the This is equivalent to calculating the mean binary outcome for bets in each direction for the bin and comparing it to the profitability thresholds of 0.

The paper finds that many bins have sample means that are outside the 0. There is no analysis of whether we can reject a null hypothesis that the population means fall within the 0. Put another way, in order for a bettor to have earned a positive profit, they would have had to know in advance of the sample period which specific bins would yield outcome probabilities below 0.

That the probability of an outcome being above or below a specified point spread or total, and therefore the quantiles of the distribution of the point spread or total, is central to the profitability of sports betting strikes me as completely obvious to everyone. PLOS authors have the option to publish the peer review history of their article what does this mean. If published, this will include your full peer review and any attached files.

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Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. We recommend that it should be revised by taking into account the changes requested by Reviewers. I want to give you a chance to revise your manuscript. The Academic Editor will only review the manuscript in the next round to speed the review process.

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If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Does technical analysis for sports betting work Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Reviewer 2: Please remove the heading "Results" prior to problem formulation. All the subheadings with a question mark should be revised as normal heading page Subsections should be named without any a punctuation mark all over the paper. Materials and methods should be placed as appendix. Discussion should be named as discussion and conclusion.

Thank you for giving me the opportunity to review this manuscript. I would like to underline that — in my opinion — the manuscript has merit and I am very confident that it will be worth publishing if several revisions are made to the manuscript. I will only very briefly mention the positive aspects of the manuscript as this review is intended to put more effort on possibilities for improvement.

However, I would like to underline that I really enjoyed reading the manuscript. I like the fact that theoretical considerations are combined with empirical data. The theory is explained intuitively and is easy to follow, the results are well-explained and graphically represented. Please see my several critical comments as an effort to further improve the manuscript.

My major point of criticism is the integration of the present results into the existing literature. Please find more details on this point below. Please be more careful and precise here. Please also find more information on this point below. In my mind this is simplifying as the results of market efficiency papers can point into different directions.

So I would suggest to be more careful here by stating that market efficiency has been the subject of investigation or more precise by saying what the important insights were. Moreover, the author states three papers from , and Below, I have summarised some more recent papers that might be worth considering:.

Angelini, G. Efficiency of online football betting markets. International Journal of Forecasting, 35 2 , Bernardo, G. Semi-strong inefficiency in the fixed odds betting market: Underestimating the positive impact of head coach replacement in the main European soccer leagues. The Quarterly Review of Economics and Finance, 71, Meier, P. Are sports betting markets semistrong efficient?

International Journal of Sport Finance, 16 3. From my point of view, this is overselling the novelty of the current manuscript. Kelly, J. A new interpretation of information rate. Snowberg, E. Explaining the favorite—long shot bias: Is it risk-love or misperceptions?. Journal of Political Economy, 4 , Moreover, it is well established in the forecasting literature to use and test several models for deciding on bets such as several stake sizes e.

Hvattum, L. Using ELO ratings for match result prediction in association football. International Journal of forecasting, 26 3 , Wunderlich, F. Are betting returns a useful measure of accuracy in sports forecasting?. International Journal of Forecasting, 36 2 , For example, Theorem 2 is highly related to the area of no profitable bet presented in the aforementioned paper.

In general, I see a lot of overlap between the two papers, both analysing betting decisions both from a theoretical point and based on real-world data. The author could also describe how the current manuscript is different from the aforementioned paper, e. Beating the market with a bad predictive model. International Journal of Forecasting, 39 2 , Results Problem formulation point spread: You mention the word point spread betting before giving an explanation on how such bets work.

You might want to give a very brief explanation on this before, particularly as a lot of literature in this domain is concentrated on European sports betting markets, where point spreads are not that pronounced. I am neither convinced that this is true nor convinced that this is false.

But I am a bit sceptical as this is obviously a strong assumption needed for the further proof. Could you discuss this issue and explain in more detail why this assumption is reasonable. This is also reflected in the literature, which for example in soccer is highly concentrated on home, draw, away betting. I would also suggest to state possible differences between European home, draw, away and North American moneyline betting.

Koopman, S. Forecasting football match results in national league competitions using score-driven time series models. Constantinou, A. Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries. Journal of Quantitative Analysis in Sports, 9 1 , Is it data provided by a company or data openly available online?

However, I would like to see this explained more clearly. Same paragraph: This paragraph states means and medians from the data. While the manuscript generally correctly underlines the potential difference between mean and median e. I would highly like to see this aspect explained and acknowledged. This claim, in my mind, is at least misleading. While I agree that a high standard deviation indicated frequent blowouts, this has nothing to do with the mean margin of victory which is rather an estimate of home advantage.

If the data would show 0. I wonder and I would like to see discussed whether there might be additional incentives of the bookmaker that contradict perfect forecasting. If you wander off the trading platform in order to draw some fancy technical indicator, you might have lost the opportunity for a profitable trade. Technical analysis can help you make money during in-play trading, if it is applied before the sport event goes live.

Before kick off the sports trader must analyze the betting graph of his choice, draw the lines he finds most suitable and add the indicator that works best for him. The second the game turns in-play, he should focus on placing bets and entering trades fast and efficient. And when the odds break below the support level he had drawn 3 minutes ago will definitely pay off.

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