The Indian sports industry is undergoing a substantial transformation in fan engagement, driven by evolving trends and technological innovations. This study comprehensively analyses the current state, methodologies, and implications of fan engagement within the Indian sports sector. In response to the COVID-19 pandemic, there has been a noticeable shift in traditional fan behaviour, with a decline in physical gatherings and a surge in alternative forms of participation such as co-watching, online discussions, sports betting, and content sharing. The research employs a multifaceted methodology, combining data collection, surveys, and trend analysis. It explores the Impact of cutting-edge technologies like Over-the-Top (OTT) media services, Non-Fungible Tokens (NFTs), blockchain technology, Artificial Intelligence (AI), and Virtual Reality (VR) on reshaping fan engagement. The dynamic and tech-driven nature of the Indian sports industry necessitates a holistic understanding of contemporary fan engagement strategies. This study aims to analyze the current landscape of fan engagement in Indian sports, explore methodologies employed, and assess the Impact of technological innovations on fan behaviour. Quantitative methods like data collection through surveys have been employed to gain insights into emerging trends and their influence on fan engagement. Survey data reveals the enduring dominance of cricket (46%) and the growing prominence of football (29%) among Indian sports fans. Notably, there is significant trust (46%) in in-game analysis technologies, indicating fans' readiness to embrace technological enhancements. While live stadium experiences remain popular, the survey underscores the role of digital platforms, with 57% preferring Hotstar for sports content. The rising popularity of fantasy league apps and the recognition of social media's Impact on player performance (64%) present opportunities for digital engagement. The study concludes by offering recommendations for businesses and stakeholders to adapt to the changing landscape. It underscores the importance of integrating innovative technologies, fostering online fan communities, and tailoring content and experiences to cater to the evolving expectations of Indian sports enthusiasts.
Gambling sponsorships are common in international soccer due to the substantial funds they provide to clubs. For example, in the 2022/23 English Premier League season, eight clubs collectively received an estimated ÂŁ60 million from gambling shirt-front sponsorships.While the Premier League plans to ban gambling shirt-front sponsorships by 2026, this will not include shirt sleeves or pitch-side hoardings, which are the most frequently seen forms of in-game marketing. In contrast, Italy and Spain have fully banned gambling sponsorships and in-game marketing due to public health concerns. Relatedly, much less attention has been paid to the emergence of sponsorships associated with cryptocurrency or financial trading. These are both gambling-like products, which are engaged in disproportionately by those experiencing gambling-related harm, and which also use soccer to market themselves. Researchers have suggested that these products might look to fill the gap in high- level sports left by gambling sponsorship bans, and so have highlighted the need to monitor their use of sponsorship agreements with high-level soccer teams. We therefore provide an overview of gambling and gambling-like sponsorship of soccer teams within high-level leagues across England, France, Germany, Spain, Italy, Portugal, and Argentina. Overall, our findings indicate that gambling sponsorship remains prominent, but has reduced in comparison to previous seasons across most countries. However, we have observed betting âpartnershipsâ which circumvent gambling sponsorship prohibitions in Italy. In relation to cryptocurrency and financial trading companies, there are limited numbers of active sponsorships outside of the UK, but âpartnershipsâ between teams and these companies have become prevalent.
Jamie Torrance, Conor Heath, Maira Andrade, Philip Newall
Background & aims: The gamblification of UK football has resulted in a proliferation of in-game marketing associated with gambling and gambling-like products such as cryptocurrencies and financial trading apps. The English Premier League (EPL) has in response banned gambling logos on shirt-fronts from 2026 onward. This ban does not affect other types of marketing for gambling (e.g., sleeves and pitch-side hoardings), nor gambling-like products. This study therefore aimed to assess the ban's implied overall reduction of different types of marketing exposure. Methods: We performed a frequency analysis of logos associated with gambling, cryptocurrency, and financial trading across 10 broadcasts from the 2022/23 EPL season. For each relevant logo, we coded: the marketed product, associated brand, number of individual logos, logo location, logo duration, and whether harm-reduction content was present. Results: There were 20,941 relevant logos across the 10 broadcasts, of which 13,427 (64.1%) were for gambling only, 2,236 (10.7%) were for both gambling and cryptocurrency, 2,014 (9.6%) were for cryptocurrency only, 2,068 (9.9%) were for both cryptocurrency and financial trading, and 1,196 (5.7%) were for financial trading only. There were 1,075 shirt-front gambling-associated logos, representing 6.9% of all gambling-associated logos, and 5.1% of all logos combined. Pitch-side hoardings were the most frequent marketing location (52.3%), and 3.4% of logos contained harm-reduction content. Discussion & Conclusions: Brand logos associated with gambling, cryptocurrency, and financial trading are common within EPL broadcasts. Approximately 1 in 20 gambling and gambling-like logos are subject to the EPL's voluntary ban on shirt-front gambling sponsorship.
Purpose This paper provides a thorough examination of Socios.com, a blockchain platform that integrates token sales with the fan experience in the sports industry. The study focuses on three key aspects: the performance, bubble phenomenon and dynamics of fan tokens. The author aims to address important questions that may concern potential supporters and investors. Might sports fans incur financial losses due to their team loyalty? Is the fan token market just a passing trend? Are fan tokens driven by the behaviour of the cryptocurrency market? Design/methodology/approach This analysis aims to involve several methodologies. The author evaluates the short- and long-term performance of fan tokens by computing first-day and buy-and-hold (abnormal) returns. The author also employs the Phillips, Shi, and Yu's (PSY) real-time bubble detection method to investigate the presence of bubble phenomenon in the fan token market segment. Finally, the author examines the potential dependences between fan tokens, Chiliz and the cryptocurrency market (represented by the CCi30 index) using both Pearson/Kendall correlations and the wavelet coherence approach. Findings The study presents three notable contributions to the existing literature. First, the author demonstrates that investing in fan tokens to support one's favourite sports teams can lead to financial losses, whereas traders can potentially outperform the market by investing in Chiliz. Second, the author states that fan tokens were a short-lived trend, as evidenced by their decline in value after the bubble burst in 2021. Third, the findings indicate that the fan token market was influenced by the cryptocurrency market and Chiliz during periods of market downturns. Originality/value To the best of authorâs knowledge, this is the first paper to conduct a comprehensive analysis of the performance, bubble phenomenon and dynamics of the token market fan segment, along with the exclusive on-platform currency, Chiliz.
Abstract This statement presents the authorâs propositionââLetâs be more conceptual!ââin response to the attempt to interpret Non-Fungible Tokens (NFTs) as contemporary art. In the context of NFTs, this opinion has the significance of finding artistry in the underlying decentralized autonomous consensus-building, and in the context of contemporary art, it has the significance of leading to the revival of early conceptual art. The second half of this statement covers the novelty and feasibility of this opinion, referring to precedents in art and engineering.
Mohak Goyal, Geoffrey Ramseyer, Ashish Goel, David Mazières
Constant Function Market Makers (CFMMs) are a tool for creating exchange markets, have been deployed effectively in prediction markets, and are now especially prominent in the Decentralized Finance ecosystem. We show that for any set of beliefs about future asset prices, an optimal CFMM trading function exists that maximizes the fraction of trades that a CFMM can settle. We formulate a convex program to compute this optimal trading function. This program, therefore, gives a tractable framework for market-makers to compile their belief function on the future prices of the underlying assets into the trading function of a maximally capital-efficient CFMM. Our convex optimization framework further extends to capture the tradeoffs between fee revenue, arbitrage loss, and opportunity costs of liquidity providers. Analyzing the program shows how the consideration of profit and loss leads to a qualitatively different optimal trading function. Our model additionally explains the diversity of CFMM designs that appear in practice. We show that careful analysis of our convex program enables inference of a market-maker's beliefs about future asset prices, and show that these beliefs mirror the folklore intuition for several widely used CFMMs. Developing the program requires a new notion of the liquidity of a CFMM, and the core technical challenge is in the analysis of the KKT conditions of an optimization over an infinite-dimensional Banach space.
Christoph Schlegel, Mateusz KwaĹnicki, Akaki Mamageishvili
We study axiomatic foundations for different classes of constant-function automated market makers (CFMMs). We focus particularly on separability and on different invariance properties under scaling. Our main results are an axiomatic characterization of a natural generalization of constant product market makers (CPMMs), popular in decentralized finance, on the one hand, and a characterization of the Logarithmic Scoring Rule Market Makers (LMSR), popular in prediction markets, on the other hand. The first class is characterized by the combination of independence and scale invariance, whereas the second is characterized by the combination of independence and translation invariance. The two classes are therefore distinguished by a different invariance property that is motivated by different interpretations of the numĂŠraire in the two applications. However, both are pinned down by the same separability property. Moreover, we characterize the CPMM as an extremal point within the class of scale invariant, independent, symmetric AMMs with non-concentrated liquidity provision. Our results add to a formal analysis of mechanisms that are currently used for decentralized exchanges and connect the most popular class of DeFi AMMs to the most popular class of prediction market AMMs.
Non-Fungible Token (NFT) markets are one of the fastest growing digital markets today, with the sales during the third quarter of 2021 exceeding $10 billions! Nevertheless, these emerging markets - similar to traditional emerging marketplaces - can be seen as a great opportunity for illegal activities (e.g., money laundering, sale of illegal goods etc.). In this study we focus on a specific marketplace, namely NBA TopShot, that facilitates the purchase and (peer-to-peer) trading of sports collectibles. Our objective is to build a framework that is able to label peer-to-peer transactions on the platform as anomalous or not. To achieve our objective we begin by building a model for the profit to be made by selling a specific collectible on the platform. We then use RFCDE - a random forest model for the conditional density of the dependent variable - to model the errors from the profit models. This step allows us to estimate the probability of a transaction being anomalous. We finally label as anomalous any transaction whose aforementioned probability is less than 1%. Given the absence of ground truth for evaluating the model in terms of its classification of transactions, we analyze the trade networks formed from these anomalous transactions and compare it with the full trade network of the platform. Our results indicate that these two networks are statistically different when it comes to network metrics such as, edge density, closure, node centrality and node degree distribution. This network analysis provides additional evidence that these transactions do not follow the same patterns that the rest of the trades on the platform follow. However, we would like to emphasize here that this does not mean that these transactions are also illegal. These transactions will need to be further audited from the appropriate entities to verify whether or not they are illicit.
We introduce the SPRIG (Smart Proofs via Recursive Information Gathering) protocol. SPRIG allows agents to propose, question, and defend mathematical proofs in a decentralized fashion. A structure of stakes and bounties aims at producing debates in good faith and if those persist, they must go down to machine-level details, where they can be settled automatically. This combination of economic incentives and an oracle is designed to promote succinct and informative proofs. SPRIG can run autonomously as a smart contract on a blockchain platform, and hence it does not rely on a central trusted institution. We translate SPRIG into a general game-theoretic model and prove that the protocol satisfies two desirable properties: no spamming and monotonicity. We then characterize analytically the equilibrium of a simple two-player specification of the model: this provides important insights into the impact of the protocolâs parameters on the probabilities that it induces type I/II errors. We conclude by discussing the main attacks SPRIGâs designers will need to take into account.
This article investigates the emerging segment of the cryptocurrency market related to football fan tokens (FFTs)âdigital assets used for engagement with professional football clubs around the world. More specifically, the authors study the investability of FFTs from the perspective of risk and return. They find that FFTs generate a whopping 150% return on the first trading day. This return is significantly larger if the FFT market cap is higher, the FFT offer price is lower, the football team displays better historical performance, and the team is located in a relatively small metropolitan area with a high GDP per capita. They also find that in the long run, FFTs severely underperform all major crypto benchmarks, including NFT, DeFi, Meme, and bitcoin. Moreover, the returns to FFTs tend to be highly volatile (160% annualized). Intriguingly, they show that the real-life performance of football teams does not affect the contemporaneous market performance of their FFTs.
Non-fungible tokens (NFTs) have gained considerable media attention and sparked growing public interest. NFTs are unique units of data recorded on a permanent ledger or blockchain. NFTs are used to record ownership of both physical and digital goods. Prominent sport organizations have embraced NFTs for innovative growth opportunities such as generating revenue via novel digital products (e.g., digital collectibles). For example, the National Basketball Association (NBA) launched NBA Top Shot, an online marketplace to buy and sell digital sports highlights. Sport organizations are exploring future innovation opportunities where there is a need to reliably track and verify authenticity or ownership of digital or digitizable assets. This includes existing sport products (e.g., tickets) and novel fan engagement initiatives. To benefit from NFTs, sport managers need to reconceptualize how sport is marketed and managed in a digital domain. The purpose of this research primer is to acquaint readers with key concepts related to NFTs. Specifically, we provide an overview of NFTs, offer a review of the brief history of NFTs, conceptualize NFTs via parallels with collectibles, and address the speculative nature of the NFT market. We conclude by outlining innovative growth opportunities of NFTs for sport managers and future research directions for sport management scholars.
Open access
5 source records
Digital Games and Media
Sports Analytics and Performance
Consumer Behavior in Brand Consumption and Identification
The statistical concept of gamblerâs ruin suggests that gambling has a large amount of risk. Nevertheless, gambling at casinos and gambling on the Internet are both hugely popular activities. In recent years, both prospect theory and laboratory-controlled experiments have been used to improve our understanding of risk attitudes associated with gambling. Despite theoretical progress, collecting real-life gambling data, which is essential to validate predictions and experimental findings, remains a challenge. To address this issue, we collect publicly available betting data from a DApp (decentralized application) on the Ethereum blockchain, which instantly publishes the outcome of every single bet (consisting of each betâs timestamp, wager, probability of winning, userID and profit). This online casino is a simple dice game that allows gamblers to tune their own winning probabilities. Thus the dataset is well suited for studying gambling strategies and the complex dynamic of risk attitudes involved in betting decisions. We analyse the dataset through the lens of current probability-theoretic models and discover empirical examples of gambling systems. Our results shed light on understanding the role of risk preferences in human financial behaviour and decision-makings beyond gambling.
Automated market makers, first popularized by Hanson's logarithmic market scoring rule (or LMSR) for prediction markets, have become important building blocks, called 'primitives,' for decentralized finance. A particularly useful primitive is the ability to measure the price of an asset, a problem often known as the pricing oracle problem. In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers (or CFMMs) which includes existing popular market makers such as Uniswap, Balancer, and Curve, whose yearly transaction volume totals to billions of dollars. We give sufficient conditions such that, under fairly general assumptions, agents who interact with these constant function market makers are incentivized to correctly report the price of an asset and that they can do so in a computationally efficient way. We also derive several other useful properties that were previously not known. These include lower bounds on the total value of assets held by CFMMs and lower bounds guaranteeing that no agent can, by any set of trades, drain the reserves of assets held by a given CFMM.
Augur is a trustless, decentralized platform for prediction markets. It is an extension of Bitcoin Core's source code which preserves as much of Bitcoin's proven code and security as possible. Each feature required for prediction markets is constructed from Bitcoin's input/output-style transactions.
In 5-Card Draw, one of the most popular versions of poker, you start with a specific question: âCan I win with the cards I have decided to play?â The final answer is yes or no. After looking at your initial cards (initial findings) you can be satisfied with what you have (preliminary data) or seek some new cards (new experiments). But in the end you must openly âshow your cardsâ (results). Your cards give you the answer. You cannot hide a card, nor can you add an undealt card to make your hand look better. Playing poker and writing the Results section of a scientific paper have similarities, as I will point out in this article. In poker, how you present your cards affects how your competitors grasp the importance of the cards. One winning set of cards in poker is the straight, defined as 5 consecutively sequenced cards (e.g., 6, 7, 8, 9, 10). You may have this group of cards, but if you present them as 6, 10, 8, 7, 9, your straight is not immediately evident. The worth of cards when presented in a logical manner is clearer and easier to grasp. The same holds true for your Results section. Your important results may be better understood if presented in a certain order. There are several options for the presentation order of results (Table 1 ); one may work better than another for the type of study being reported. The most straightforward approach is to use a chronological order with subheadings that parallel the methods and their sequence presented earlier in the paper. This order allows readers to more easily go back and refer to the methods associated with a given result. Options for presentation order of results. Options for presentation order of results. A second approach is to group results by topic/study group or experiment/measured parameter. An example of this format is a comparison of the diagnostic and analytical performance of 3 assays for serum prostate-specific antigen. If grouped by assay as the topic, the results for diagnostic accuracy, analytical performance, interference testing, and cost analysis for assay 1 would be presented first, followed by a separate presentation of the same results for assay 2 and then assay 3. This order allows the reader to see the results for each assay as a packet of information, which is a logical way to remember information. By comparison, if the results are grouped by measured parameter, important similarities or differences in assay performance may be clearer and can be emphasized as important findings. Grouped by topic: Assay 1: diagnostic accuracy, performance, interferences, cost. Assay 2: diagnostic accuracy, performance, interferences, cost. Assay 3: diagnostic accuracy, performance, interferences, cost. Grouped by measured parameter: Diagnostic accuracy: assay 1, assay 2, assay 3. Performance: assay 1, assay 2, assay 3. Interferences: assay 1, assay 2, assay 3. Cost: assay 1, assay 2, assay 3. In clinical studies that involve multiple groups of individuals or patients receiving different treatments, it is common to order the results from general to specific. The characteristics of the overall study population, such as sex and age distribution, initial and final numbers in each group, and dropouts are first presented. This information is followed by the data and results for each specific group, i.e., starting with the control group or the group receiving the standard treatment, followed by the results for the disease group or the group receiving the experimental treatment. Lastly, if you undertook a study for which the order in which the results are presented is not critical to their being understood, presenting the results from most to least important immediately highlights the results you want to emphasize. Results should be presented in the past tense. The Results section usually ends up heavier on the passive voice, but some conscious use of the active voice can help the flow and readability of the text (e.g., âwe observed that the 2 groupsâ versus âit was observed that the 2 groupsâ). One valuable lesson I learned about writing a well-crafted Results section came from Zeigerâs book, Essentials of Writing Biomedical Research Papers. The same conceptânamely, that data and results are not the sameâwas discussed more recently in an article by Foote in the journal Chest (see Resources and Additional Reading). Authors can err by offering the reader results but no data, or data but no results. Data are facts and numbers. Data are usually presented in tables and figures as raw data (individual data points) or summarized data (mean, percent, median and range). Results are statements in the main text that summarize or explain what the data show. As an example, letâs use a hypothetical study comparing the effectiveness of radiation treatment, chemotherapy with an existing drug (Blasteride), and a new monoclonal antibody-based therapy (Neuroxomab) for the treatment of neuroblastoma. One of the endpoints in the study is survival rate after diagnosis and initiation of treatment (Figure 1 ). Four ways to present the information in Figure 1 for the reader might be as follows: Two-year survival rates for patients with neuroblastoma treated with Neuroxomab, Blasteride, and radiation. Figure 1 shows the survival rates following diagnosis and initiation of treatment in the 3 treatment groups. At 6 months the survival rates were 95% for the Neuroxomab group, 91% for the Blasteride group, and 39% for the radiation-treated group. At 12 months the rates were 83%, 69%, and 23%;, at 18 months 74%, 17%, and 15%; and at 24 months were 70%, 11%, and 9%. Figure 1 shows the survival rates following diagnosis and initiation of treatment in the 3 treatment groups. At 6 months the survival rates were significantly higher in the Neuroxomab and Blasteride treatment groups compared with the radiation-treatment group. At 12, 18, and 24 months the survival rates in the Neuroxomab group exceeded those of both the Blasteride and radiation-treatment groups. Six months after diagnosis and initiation of treatment, the survival rates for the Neuroxomab and Blasteride groups were 2.4 and 2.3 times higher, respectively, than the radiation treatment group (both P < 0.001), but survival rates were not found to differ between the Neuroxomab and Blasteride groups (P = 0.56) (Figure 1 ). By 12 months, however, patient survival in the Neuroxomab group was 1.2 times higher than in the Blasteride group (P = 0.031), and 4.3 and 6.4 times higher at 18 and 24 months (both P < 0.001). Six months after diagnosis and initiation of treatment, survival rates in the Neuroxomab and Blasteride groups (95% and 91%, respectively) were significantly higher than in the radiation treatment group (39%, P < 0.001 for both), but survival rates were not found to differ between the Neuroxomab and Blasteride groups (P = 0.56) (Figure 1 ). By 12 months, however, the patient survival rate in the Neuroxomab group was significantly higher than in the Blasteride group (83% vs 69%, P = 0.031), a difference that became even greater at 18 and 24 months (74% vs 17% and 70% vs 11%; both P < 0.001). The first paragraph above provides data but no results. What do the data show? What is the point? Are the treatment groups statistically different at 6 months? The second paragraph contains results but no data. Is it clear from the figure how much higher the survival rates for patients in the Neuroxomab and Blasteride groups were compared with patients in the radiation group and with each other? What is the level of significance of any differences? Paragraphs 3 and 4 above contain both data and results. They describe the important treatment differences and report when the differences occurred and whether they were statistically significant. Paragraph 3 states the magnitude (e.g., 2.4 times higher) of the most important differences between the treatments, and whether the differences were statistically significant. The reader must look at the figure to see the percent survival data, but this is perfectly fine as long as the reader can fairly easily estimate the percentages at 6, 12, 18, and 24 months. Paragraph 4 includes the actual survival rates (e.g., 95%, 91%, and 39% at 6 months) rather than the relative magnitudes of any differences. The inclusion of these survival-rate data in this paragraph is acceptable because the figure contains a lot of information and you are highlighting selected important differences. However, letâs now say that the survival data and P-values had been provided in a table (Table 2 ). Because Table 2 contains the same information included in paragraph 4, you need not repeat this information in both places: Neuroblastoma survival rates over time for Neuroxomab, Blasteride, and radiation-therapy patient groups. P < 0.001 vs radiation group. P = 0.56 vs Blasteride. P = 0.031 vs Blasteride. P < 0.001 vs Blasteride. Not significant vs radiation group. Neuroblastoma survival rates over time for Neuroxomab, Blasteride, and radiation-therapy patient groups. P < 0.001 vs radiation group. P = 0.56 vs Blasteride. P = 0.031 vs Blasteride. P < 0.001 vs Blasteride. Not significant vs radiation group. Six months after diagnosis and initiation of treatment, the Neuroxomab and Blasteride groups showed significantly higher survival rates compared with the radiation-treatment group (Table 2 ), but survival rates in the Neuroxomab and Blasteride groups were not found to differ. By 12 months, however, patient survival in the Neuroxomab group was significantly higher than in the Blasteride group, a difference that became even greater at 18 and 24 months. This rule about nonrepetition of data is not absolute, but is a rule that should be broken only in rare circumstances. If a table or figure supplies a large amount of data, it is acceptable to restate a key piece of data in the text, such as the 2 groups in the table with statistically significant differences, if this helps the reader zero-in on an important result without having to plow through a long list of data. In the American judicial system witnesses are sworn in by asking if they will tell the truth (the facts), the whole truth (tell everything), and nothing but the truth (no lies, conjecture, or interpretation). A complete Results section in a scientific paper also satisfies these requirements. Telling the facts is the easy part, because this is the goal of this section: to tell the reader what you found during the study. Requirements 2 and 3 above are areas in which authors can run into problems. Satisfying the second requirement involves an intentional effort to include all data. There are well-crafted guidelines and checklists available that can help you meet the minimal standards for reporting data and results for many types of studies (Table 3 ). As an author you should use the checklists and flow diagrams in these guidelines when appropriate for your study. Doing so not only helps make the strengths, weaknesses, and sources of bias clear to the reader, but also helps you remember to include key data that otherwise inadvertently might have been omitted. For example, how many patients were excluded from the study? How many were lost to follow-up? How many dropouts were there? How many patients finished the study? How many individuals had an inconclusive result or diagnosis? These are all data and results and belong in the Results section. Reporting guidelines for various types of studies. Reporting guidelines for various types of studies. Including all results also means not leaving out a negative result (hiding a card) or a result relevant to the report because it serves some other purpose for you as the author. Anyone who chooses to repeat your work or use your methods will likely encounter the same type of negative results that you did, and the fact that these were not acknowledged in your paper will not serve you well. Referring to âunpublished resultsâ annoys most editors and peer reviewers unless you can present a good argument for not including them. Trying to stake a claim to a future study by presenting an attention-grabbing preliminary result, but then not showing any corresponding data, can make readers question your motive. The Results section is just that: results. To satisfy the third requirement above, this section should contain nothing but the results. No methods, no discussion. There is a temptation to remind the reader about the details of the experiment performed or the method used to generate the results, especially if it has been several pages since the Methods section ended. Method, study, and experimental details should not be restated in the Results section. Of course, you can refer to a specific experiment or method when describing the corresponding results; just do not repeat experimental details already described in the Methods section, as exemplified below. Although well intended as a link between a method and a result, the first 2 sentences of the next paragraph are unnecessary: We compared the death rates for the 262 healthy controls with those of the 203 congestive heart failure patients over a 2-year period. Survival curves were generated with the Masterson mortality index formula. The congestive heart failure group was found to have a significantly higher short-term mortality rate. However, this example is a good opportunity to illustrate how a transition phrase can serve as a link between a previously described experiment and a result without repeating what was in the Methods section: When the 2-year survival curves for healthy controls and congestive heart failure patients were compared, the congestive heart failure group was found to have a significantly higher short-term mortality rate. The only time that experimental details are appropriate for the Results section is when the initial experiments (rightly described in the Methods section) yield data that lead to additional experiments, not part of the original protocol, but which became necessary later on. The description of these experiments may make more sense if included in the Results section with the corresponding results. When reporting results, authors feel an urge to comment on the results, e.g., how the results compared with prior work, were consistent with what was predicted in another paper, or explained the reason that a marker is increased in a disease. The interpretation or analysis of the results, however, belongs in the Discussion section. In the Results section you can describe what the data show, in the Discussion section you describe what the data mean. The purposely incorrect heading here is meant to emphasize the fact that the terms significant, significance, and significantly are used erroneously in many submitted papers. In biomedical publications these terms are intended to identify relationships that have been statistically tested and determined unlikely to have occurred by chance. These terms should also be followed by a mathematical value or limit (e.g., P = 0.067 or P < 0.001). Unless you have such proof of statistical significance, you should use other terms such as substantial, considerable, or noteworthy. Similarly, authors like to draw unwarranted attention to nonsignificant findings by stating that the data âtrended towardâ or âtended to show.â If the findings are not clear, donât try to imply something about them that cannot be supported. Lastly, make sure that the Results section is consistent with all of the other sections in the final version of your paper. Is there a result that does not have a corresponding method or experiment in the Methods section? Conversely, is there a method or experiment for which you have reported no results? Is there a result not covered in the Discussion section, or discussion of a result not contained in the Results section? Are the most important results the same as those highlighted in the Abstract? Do the results relate to the study question, hypothesis, or problem first presented in the Introduction? 1. Point out which information is data and which is a result in the following paragraph: Baseline median IL-6 concentrations were 12, 26, 96, and 144 Îźg/L for categories 1 to 4, respectively, and were not found related to age or sex. Median β-selectin concentrations increased 30% across the 4 categories. Increased disease severity and mortality were associated with higher IL-6 concentrations, but not β-selectin. Intraindividual variation for group 1 was 14% for IL-6 and 36% for β-selectin. 2. Choose whether the presentation of results in the following sentence is chronological, grouped by topic/study group, grouped by experiment/measured parameter, general to specific, or most to least important: The mean (SD) admission interleukin concentrations were 13.6 (1.4) Îźg/L, 10.3 (1.1) Îźg/L, and 3.6 (05) Îźg/L in the coronary bypass graft, percutaneous intervention, and congestive heart failure patient groups, respectively. 3. Pretend that a journal editor has decided that you must remove Table 4 from your paper and place the information it contains into the main text. How might you write a paragraph that presents the data and results in this table? Serum antiproxin concentrations in patients with congestive heart failure. P = 0.019 vs healthy patients. P < 0.001 vs healthy patients. P = 0.017 vs asymptomatic heart failure. Serum antiproxin concentrations in patients with congestive heart failure. P = 0.019 vs healthy patients. P < 0.001 vs healthy patients. P = 0.017 vs asymptomatic heart failure. A Results section that clearly presents your results, makes effective use of both data and results, includes all the important results, and does not wander off into discussion of the results, will result in a better paper and a greater chance of its acceptance for publication. In the end, isnât that the result you are looking for? Foote M. The proof of the pudding: how to report results and write a good discussion. Chest 2009; 135:866â8. Huth EJ. Writing and publishing in medicine. Baltimore: Williams and Wilkins; 1999. Katz MJ. From research to manuscript. New York: Springer; 2009. Lang TA. How to write, publish, and present in the health sciences. Philadelphia: ACP Press; 2010. Zeiger M. Essentials of writing biomedical research papers. New York: McGraw Hill; 2000. Baseline median IL-6 concentrations were 12, 26, 96, and 144 Îźg/L for categories 1 to 4, respectively [DATA], and were not found related to age or sex [RESULT]. Median β-selectin concentrations increased 30% across the 4 categories [RESULT]. Increased disease severity and mortality were associated with higher IL-6 concentrations, but not β-selectin [RESULT]. Intraindividual variation for group 1 was 14% for IL-6 and 36% for β-selectin [DATA]. The presentation is grouped by experiment/measured parameter, which is the mean admission interleukin concentration. Even though the data are presented from the highest (13.6 Îźg/L) to the lowest (3.6 Îźg/L) value, the higher value is not necessarily the most important finding. Median (interquartile range) serum antiproxin concentrations were 99 (36â144), 216 (147â296), and 556 (328â791) ng/L in healthy individuals, asymptomatic heart failure patients, and symptomatic heart failure patients, respectively. The median concentrations in asymptomatic and symptomatic heart failure patients were 2.2-fold higher (P = 0.019) and 5.6-fold higher (P < 0.001), respectively, than in healthy individuals, and symptomatic patients had significantly higher serum antiproxin concentrations compared with asymptomatic patients (P = 0.017). Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. Authorsâ Disclosures of Potential Conflicts of Interest:Upon manuscript submission, all authors completed the Disclosures of Potential Conflict of Interest form. Potential conflicts of interest: Employment or Leadership: T.M. Annesley, AACC. Consultant or Advisory Role: None declared. Stock Ownership: None declared. Honoraria: None declared. Research Funding: None declared. Expert Testimony: None declared. Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or preparation or approval of manuscript.