{"id":"3b1ff1aa-4bc2-4953-894d-ff6a19869393","arxiv_id":"2607.24573","paper_version":1,"verdict":"ACCEPT","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A prospective benchmark of 8,736 World Cup match forecasts shows that seven frontier LLMs perform comparably, web access adds a small Brier-score improvement, and prompting order and forecast horizon have little effect.","lead":"This paper introduces LLM-SoccerArena, an open-source platform that records forecasts from seven large language models for all 104 matches of the 2026 FIFA World Cup before kickoff. It reports that enabling web search improves match forecasts by a small but statistically significant margin, while differences between models are mostly small.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The open-book Brier gain may reflect prompt-wording differences rather than retrieved information; a tool-only ablation is needed to support the causal claim.","rationale":"The reader's weakest_assumption identifies exactly the concern I consider load-bearing: Appendix F.3 shows that open-book and closed-book prompts differ in wording beyond tool availability. My stress-test agrees that this is the point on which the central empirical claim is least secure. The paper's own robustness checks do not resolve it: leave-one-match-out and timing-window analyses keep the same prompt wording between conditions, and the observed-search sensitivity analysis compares open-book forecasts with observed search against closed-book forecasts, again with different prompts. The evidence-category analysis shows that open-book rationales mention recent form, markets, and injuries far more often, but this measures generated text, not causal influence, and the paper itself states that rationales do not establish causal use of information. The result is a small effect (0.0228 Brier, a 4.3% reduction) with an adjusted p-value just below 0.05; a wording-induced shift of only a few hundredths could fully account for it. I found no other comparably load-bearing concern: the benchmark protocol is genuinely prospective, the statistical procedures are prespecified and reproducible, the external market baseline is handled transparently, and the model-comparison null results are appropriately cautious. The platform contribution stands on its own, but the headline 'web access improves forecasts' is a causal claim that the current design does not fully support. Therefore I would adjust the verdict from ACCEPT to CONDITIONAL: accept the benchmark and its descriptive findings, but require a tool-only wording ablation, or a rephrased claim, before the causal interpretation is presented as established.","tokens_in":31800,"tokens_out":4763,"duration_ms":48009,"concrete_test":"Run a matched ablation on the same platform: for a random subset of at least 50 matches, collect three conditions per model: (i) the current closed-book prompt with web search disabled; (ii) the current open-book prompt with web search enabled; and (iii) the current open-book prompt text verbatim but with web search disabled (no tool available), so the model cannot retrieve anything. Compare paired Brier differences within the same model and match. If (iii) minus (i) is near zero and (ii) minus (iii) reproduces the 0.0228 improvement, the attribution to retrieved information is supported. If (iii) minus (i) is comparable to the full effect, the prompt wording explains the gain and the claim must be reworded to 'open-book condition improves forecasts' rather than 'web access improves forecasts.'","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The paper's headline empirical claim is that web access improves forecast quality: at T–24h, mean Brier falls from 0.535 (closed-book) to 0.512 (open-book), a paired difference of 0.0228 (95% CI [0.0044, 0.0403], Holm-adjusted p=0.045; §4.3). The factorial design is intended to isolate information access, but Appendix F.3 shows the two conditions differ in more than tool availability. The closed-book prompt says 'Do not use internet search, browsing, tools, APIs... Use only the match information below plus your internal football knowledge,' while the open-book prompt says 'You may use the available web-search tool... Base the final forecast on public information, the match information below, and calibrated football reasoning.' These are different reasoning instructions, not just different tool availability: the open-book prompt explicitly directs the model to ground its forecast in public information and labels the condition OPEN_BOOK. A model given this instruction could produce better-calibrated probabilities without retrieving anything, because the wording changes how it weighs internal knowledge and expresses uncertainty. The observed-search and evidence-category analyses (Appendix C.3, Appendix D) are consistent with an information mechanism, but they are correlational: every open-book forecast receives both the tool and the wording, so they cannot separate the two. Since the measured effect is small and only marginally significant after Holm correction, a modest wording-induced calibration shift could account for the entire difference. This does not undermine the benchmark infrastructure or the descriptive results, but it does undermine the causal interpretation 'web access improves forecasts' unless the wording confound is controlled.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces LLM-SoccerArena, an open-source, prospective live benchmark for evaluating LLM forecasts of unresolved real-world sports events, demonstrated on all 104 matches and 15 tournament questions of the 2026 FIFA World Cup. The platform records timestamped, schema-validated forecasts together with prompts, model versions, tool traces, and costs, and implements a factorial design over model version, information access, prompting strategy, and forecast horizon. The case study evaluates seven LLMs with 8,736 match forecasts and 420 tournament forecasts, reporting that open-book access improves mean Brier score by 0.0228 (from 0.535 to 0.512) at T-24h, that no model clearly wins, that prompting strategy has no average effect, and that LLM forecasts are competitive with de-vigged closing bookmaker odds. The paper also provides extensive reproducibility artifacts, including frozen snapshots, hashes, validation logs, and code.","tokens_in":31971,"tokens_out":5311,"duration_ms":50904,"significance":"If the empirical claims are supported, this is a valuable contribution: it provides a standardized, auditable, and continuously operating benchmark for a class of real-world forecasting tasks, with a transparent prospective protocol and a strong set of reproducibility practices. The paper's strengths include machine-checked artifact provenance, frozen analysis snapshots, matched paired comparisons with bootstrap and permutation inference, Holm correction within declared families, leave-one-match-out robustness checks, and an external de-vigged odds baseline. The comparison of LLMs to a market baseline is a useful reference point. However, the headline causal claim about web access is not yet cleanly identified, because the open-book and closed-book conditions differ in prompt wording as well as tool availability.","major_comments":[{"comment":"The headline open-book improvement (mean Brier 0.535 to 0.512; paired difference 0.0228, 95% CI [0.0044, 0.0403], Holm-adjusted p=0.045) is attributed to 'web access,' but the two conditions differ in prompt wording as well as tool availability. The closed-book prompt instructs the model to use only the match information plus internal knowledge, while the open-book prompt instructs it to use the web-search tool, to 'base the final forecast on public information,' and it labels the condition OPEN_BOOK. These wording differences can affect calibration independently of retrieved information, so the measured effect is not a clean estimate of information access. Because the effect is small and only marginally significant, a tool-only vs. wording-only ablation (e.g., open-book wording with the search tool disabled) is needed before the causal claim in the abstract and Section 4.3 can be sustained; alternatively, the claim should be softened to an association.","section":"§4.3, Appendix F.3"},{"comment":"The intent-to-treat analysis includes open-book calls in which no search was observed (search was observed in only 84.5% of open-book forecasts), and the secondary observed-search sensitivity analysis compares self-selected groups with model-specific search propensities. That analysis is correlational and cannot separate the effect of retrieved information from the effect of the open-book wording; for example, if the wording alone improves calibration, the observed-search subset would show an effect even when no information is retrieved. The Limitations section correctly states that generated rationales do not establish causal influence, but this caution is not applied to the headline information-access claim, which is stated causally in the abstract and in Section 4.3.","section":"§4.3, Appendix C.3"}],"minor_comments":[{"comment":"The row label 'Ex.source' appears truncated; it should read 'External sources' or a more descriptive phrase.","section":"Table 1"},{"comment":"In the first paragraph, 'i.e., )LLM evaluation' contains a stray parenthesis, and the sentence introducing the three research streams is awkwardly punctuated.","section":"Section 2"},{"comment":"The sentence 'Continuous operation enables evaluation at scale and create a longitudinal record' should be 'creates a longitudinal record.'","section":"Appendix A"},{"comment":"The open-book prompt instructs the model not to use the project's stored predictions; this is an instruction that cannot be independently verified from the recorded tool traces, so it should be listed as a limitation of the audit trail.","section":"Appendix F.3"},{"comment":"The model registry honestly reports 'Public weights: Unverified' for all models; the paper should briefly explain the implications for third-party reproduction, since exact deployed endpoints cannot be independently reconstructed.","section":"Table 3"}],"recommendation":"major_revision","confidential_remarks":"The paper is well-engineered and the platform contribution is strong. The main risk is the wording confound in the open-book versus closed-book comparison, which directly affects the paper's headline empirical result. I recommend asking the authors to run a tool-only control condition or to reframe the claim as associative; either fix is within scope and would make the paper acceptable."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague — this is a good benchmark paper, and I'd send it out. The new thing is the infrastructure: a prospective, timestamped, schema-validated forecast collection pipeline with a public archive, tool traces, costs, validation and repair records, and a factorial design over models, information access, prompting, and horizon. The 8,736-match World Cup deployment is real prospective data, and the statistics are careful: matched paired tests, bootstrap and permutation inference, Holm correction within declared families, leave-one-match-out robustness, and an external de-vigged closing-odds baseline. The main empirical payoff is not a model winner — there isn't one, which is itself useful — but the open-book/closed-book contrast: mean Brier improves by 0.023, Holm-adjusted p = 0.045. Gemini's open-book T-2h forecasts land almost exactly at the market consensus (0.497 vs 0.498 Brier), a sensible sanity check.\n\nWhere I part company with the authors' interpretation: the web-access effect is plausible but the causal reading is under-supported. Appendix F.3 shows the closed and open prompts differ in more than tool availability. Closed-book says use only the match information plus internal knowledge; open-book says you may search and also tells the model to base the final forecast on public information. Those are different reasoning instructions, and a model may calibrate differently just from being told to ground its forecast in public information. The observed-search and evidence-category analyses are consistent with an information mechanism, but they are correlational — every open-book forecast gets both the tool and the wording. Given the effect is small and only marginally significant after Holm, a wording-induced calibration shift could explain much of it. The paper concedes rationales are not causal but still states 'web access improves forecasts' as a key finding. I would want a tool-only ablation — same wording with the search tool disabled, or a dummy tool — before trusting the causal claim.\n\nThis does not sink the benchmark. The descriptive results, the null model-comparison result, and the platform itself stand. Minor oddity: Claude Fable's partial coverage is attributed to U.S. sanctions with no further detail; fine as archive-only, but unexplained. The tournament-question probability-sum audit shows some forecasts violate constraints, and they handle it transparently. Self-citations are for reporting checklists and NLP methods, not self-serving.\n\nBottom line: solid infrastructure paper with a soft causal claim. It belongs in front of a serious referee. I'd accept with revisions focused on the open-book prompt wording, and require the ablation before the causal language stays.","headline":"A genuinely prospective, open benchmark with careful stats; the headline web-access gain is plausible but the prompt-wording confound means the causal claim needs softening.","tokens_in":32620,"tokens_out":2565,"would_cite":true,"duration_ms":23968,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":["68T50","62F15","62P30"],"pacs":[],"model":"deepseek-v4-flash","headline":"A live benchmark tests LLMs on World Cup matches before the outcomes are known.","keywords":["large language models","forecasting","prospective benchmark","live benchmark","soccer","sports analytics","Brier score","web search"],"falsifier":"A reader could look at the public archive and check whether the open-book advantage of 0.0228 in Brier score is replicated on the next tournament, and more directly, could run a controlled experiment that varies the prompt-header wording independently of tool availability; if open-book headers without any actual search still produce the improvement, the information-access attribution fails.","tokens_in":31544,"feed_emoji":"⚽","tokens_out":3412,"duration_ms":26455,"temperature":0.7,"pith_summary":"LLM-SoccerArena is a prospective, open-source benchmark that records forecasts of football matches before the outcomes are known, so the results cannot be contaminated by memorized post-hoc knowledge. In its first large deployment, seven LLMs forecast all 104 matches of the 2026 FIFA World Cup and 15 tournament questions under a factorial design varying model, web access, prompt order, and forecast horizon. The central empirical claim is that giving models web access yields the clearest controlled improvement in forecast quality, lowering the mean Brier score from 0.535 to 0.512 (paired difference 0.0228, Holm-adjusted p=0.045), while no single model wins after correction and prompting order has no measurable effect. The paper argues that sports, with their scheduled, objectively resolved, continuously recurring events, are a suitable setting for testing whether LLMs can synthesize changing information into calibrated probabilistic judgments before an outcome is resolved.","feed_headline":"Web search lifts LLM forecasts of World Cup matches","feed_subtitle":"In 8,736 prospective forecasts, open-book access cut Brier error from 0.535 to 0.512; no single model won.","key_machinery":"The load-bearing mechanism is the prospective live benchmark protocol: forecasts are registered, timestamped, schema-validated, and archived while the event outcome is still unresolved, with an outcome barrier that prevents known results from entering the recorded forecasts. The factorial design Z=M×A×P×H (model version, information access, prompting strategy, forecast horizon) keeps the forecasting task identical across all configurations so that each factor can be compared on the same matches; the statistical machinery is a paired permutation test with 10,000 sign-flips and Holm correction within prespecified comparison families. The named evaluation instrument is the multiclass Brier score on the home/draw/away probability vector after 90 minutes, plus log loss, modal accuracy, exact-score accuracy, calibration curves, and a Scoring System for scorelines.","core_discovery":"The paper's central claim, stated on its own terms, is that LLM-SoccerArena provides the first prospective, live, factorial benchmark for LLM forecasting of unresolved real-world sports events, and that its first large-scale deployment yields new evidence about how information access shapes LLM forecasts. Concretely: open-book web access improves forecast quality by a small but statistically significant margin (mean Brier 0.512 vs 0.535 at T–24h; within-match closed-minus-open difference 0.0228, 95% CI [0.0044, 0.0403], Holm-adjusted p=0.045), while no model version emerges as a clear winner (Brier 0.506 to 0.546, no paired comparison significant after Holm correction) and prompt order does not change accuracy (difference −0.0008, adjusted p=0.693). The paper also reports that the seven models' forecasts are highly correlated (mean pairwise correlation 0.943, Jensen–Shannon divergence 0.0044), that equal-weight ensembling adds little, that open-book rationales mention concrete current evidence far more often (recent form +68.0 pp, markets/odds +60.2 pp, injuries/lineups +53.0 pp) and generic unsupported claims less often (−18.1 pp), and that the best open-book LLM's T–2h forecasts tie the de-vigged closing bookmaker consensus (Gemini Brier 0.497 vs market 0.498).","pith_inferences":["An implicit extension is that soccer's standardized, continuously recurring event stream lets the benchmark separate memorization from forecasting ability better than retrospective question sets, because no model can have seen the outcome.","A testable extension would be to randomize the header wording of the closed-book and open-book prompts independently of tool availability, to check whether the measured 0.0228 Brier improvement is purely an information-access effect or partly a wording effect.","The evidence-category results suggest a concrete mechanism for the open-book gain: the delivered rationales shift from generic claims to current, citable facts; verifying whether that shift predicts per-match Brier improvement would connect the qualitative and quantitative findings.","A neighbouring application is to use the same protocol benchmark for other low-scoring, widely-covered sports or for structured events with frequent unresolved outcomes, where the factorial design can be reused without modification."],"forward_implications":["If the open-book result is correct, practitioners should give LLMs web access when decisions depend on current information, rather than relying on a larger or newer model alone.","The finding that forecasting closer to kickoff adds little suggests that late-breaking information beyond T–24h is not being effectively synthesized by these models, pointing to where retrieval design matters.","If the high pairwise forecast correlation persists on future tournaments, ensemble aggregation of diverse LLMs is unlikely to yield large gains without mechanisms that incentivize genuinely distinct forecasts.","The tie with closing bookmaker odds at T–2h, if it replicates, means a frontier LLM can match an aggregated market signal on a well-covered event without dedicated sports-analytics features.","The platform itself extends to league competitions, so the same protocol can produce a longitudinal record of whether successive model generations improve forecast quality, calibration, and search behavior over time."],"supporting_citations":[{"why":"ForecastBench is the live forecasting benchmark whose design the paper extends by adding a standardized, recurring sports event stream and a factorial comparison of conditions.","marker":"[20]"},{"why":"TS-Arena is a live numerical forecasting benchmark for the energy sector, cited as one of the few existing prospective evaluation platforms.","marker":"[30]"},{"why":"Prophet Arena evaluates prediction-market events prospectively and is the closest antecedent for the live, unresolved-event evaluation design.","marker":"[41]"},{"why":"AutoCast++ reconstructs time-indexed information for retrospective forecasting, the approach LLM-SoccerArena argues cannot fully prevent leakage from post-forecast knowledge.","marker":"[40]"},{"why":"Autocast is the retrospective world-event forecasting dataset that motivates the paper's prospective design.","marker":"[43]"},{"why":"The Brier score is the paper's primary evaluation metric, defined in this classical reference.","marker":"[3]"},{"why":"Gneiting and Raftery establish the theory of strictly proper scoring rules that justifies using Brier score and log loss to evaluate the full probability distribution.","marker":"[12]"},{"why":"Leitner, Zeileis and Hornik provide the bookmaker-consensus forecasting baseline used in the closing-odds comparison.","marker":"[24]"},{"why":"Zeileis, Leitner and Hornik supply the de-vigged bookmaker consensus model for the World Cup that the paper's soccer-specific external reference is based on.","marker":"[42]"},{"why":"The permutation-based statistical testing procedure is cited as the source of the 10,000 sign-flip paired difference tests used throughout the analysis.","marker":"[11]"}],"fun_headline_variants":["Small web-search edge in LLM World Cup forecasts","LLM-SoccerArena: web access nudges forecast quality","World Cup LLM benchmark: web beats no-web by a hair","For LLM forecasters, internet access helps—just not much"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The headline attribution assumes that the open-book and closed-book conditions differ only in whether web access is available, but the two prompt headers also differ in wording, so the measured Brier improvement is not purely an information-access effect if those wording differences shift calibration independently.","fun_headline_variants_meta":{"raw":{"variants":["Small web-search edge in LLM World Cup forecasts","LLM-SoccerArena: web access nudges forecast quality","World Cup LLM benchmark: web beats no-web by a hair","For LLM forecasters, internet access helps—just not much"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000311,"raw_usage":{"total_tokens":1915,"prompt_tokens":1234,"completion_tokens":681,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":850,"completion_tokens_details":{"reasoning_tokens":609}},"tokens_in":850,"tokens_out":681,"duration_ms":6611,"temperature":1.0,"reasoning_tokens":609,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T15:26:48.311608+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A reader could look at the public archive and check whether the open-book advantage of 0.0228 in Brier score is replicated on the next tournament, and more directly, could run a controlled experiment that varies the prompt-header wording independently of tool availability; if open-book headers without any actual search still produce the improvement, the information-access attribution fails.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Prophet Arena evaluates prediction-market events prospectively and is the closest antecedent for the live, unresolved-event evaluation design."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"AutoCast++ reconstructs time-indexed information for retrospective forecasting, the approach LLM-SoccerArena argues cannot fully prevent leakage from post-forecast knowledge."},{"cited_title":"unverified","cited_arxiv_id":null,"evidence_quote":"Autocast is the retrospective world-event forecasting dataset that motivates the paper's prospective design."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"The Brier score is the paper's primary evaluation metric, defined in this classical reference."},{"cited_title":"2018.Probabilistic Forecasts for the 2018 FIFA World Cup Based on the Bookmaker Consensus Model","cited_arxiv_id":null,"evidence_quote":"Zeileis, Leitner and Hornik supply the de-vigged bookmaker consensus model for the World Cup that the paper's soccer-specific external reference is based on."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"The permutation-based statistical testing procedure is cited as the source of the 10,000 sign-flip paired difference tests used throughout the analysis."}],"review_version":1}