questions_auto.json

[
    {"question_number": "Q-01", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "Cosine", "question": "Count the successful passes did each team complete in the first half (minutes 41-47) and the second half (minutes 45-50), and what impact did these passes have on their game performance?", "question_auto": "None", "question_auto_explanation": "None: No answer found."},
    {"question_number": "Q-02", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "Cosine", "question": "Which players recorded the highest number of carries and passes in both halves, and how did their performances influence the overall strategies of the teams?", "question_auto": "Cosine", "question_auto_explanation": "**Cosine**: The question requires an analysis of player performances based on their carries and passes, which are directional and can be represented as vectors. Cosine similarity is effective for comparing the angles between these vectors, allowing us to assess which players had the most significant impact on their teams' strategies through their movement and passing. This approach will help identify players who not only had high numbers but also contributed effectively to their team's overall play style."},
    {"question_number": "Q-03", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "Cosine", "question": "Which players recorded the highest number of carries and passes in both halves in minutes 15 to 30 and impact in the game performance?", "question_auto": "Cosine", "question_auto_explanation": "**Cosine**: The cosine similarity algorithm is suitable for this analysis because it measures the angle between two vectors, which can effectively capture the similarity in the number of carries and passes made by players. Since we are interested in comparing the performance of players based on their contributions (carries and passes) in specific time frames, cosine similarity will help identify players who had a significant impact relative to others, regardless of the absolute values. This approach allows for a normalized comparison, highlighting players who consistently performed well in both halves."},
    {"question_number": "Q-04", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "Cosine", "question": "Count the number of carries and passes per team in minutes between 15 to 30", "question_auto": "None", "question_auto_explanation": "None: No answer found."},
    {"question_number": "Q-05", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "Cosine", "question": "What were the average pass lengths for each team in each half, and which team showed higher pass accuracy by comparing completed passes to incomplete ones?", "question_auto": "None", "question_auto_explanation": "None: No answer found."},
    {"question_number": "Q-06", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "Cosine", "question": "What were the average pass lengths in minutes between 30 and 45?", "question_auto": "None", "question_auto_explanation": "None: The question requires specific numerical calculations based on the events data, which involves extracting and averaging pass lengths. The provided algorithms (Cosine, Euclidean, Negative_inner_product) are not suitable for this type of analysis, as they are designed for measuring similarity or distance between vectors rather than performing arithmetic operations on numerical data."},
    {"question_number": "Q-07", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "InnerP", "question": "How many defensive actions, such as tackles, saves, and blocks, did each team execute, and show the top 3 players in these actions for each team?", "question_auto": "None", "question_auto_explanation": "None: The provided EVENTS_DATASET does not contain specific information about defensive actions such as tackles, saves, or blocks. Therefore, it is not possible to determine the number of defensive actions executed by each team or identify the top players in these categories."},
    {"question_number": "Q-08", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "Cosine", "question": "What defensive actions, such as tackles, saves, and blocks, did each team execute in second half?", "question_auto": "None", "question_auto_explanation": "None: The provided dataset does not contain specific information about defensive actions such as tackles, saves, or blocks executed by each team in the second half. The events primarily focus on passes, carries, and fouls without detailing defensive statistics."},
    {"question_number": "Q-09", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "Cosine", "question": "How many shots on goal did each team take, splitted in 30 minutes timeframes?", "question_auto": "None", "question_auto_explanation": "None: The provided datasets do not contain information about shots on goal or any related statistics. Therefore, it is not possible to determine the number of shots on goal taken by each team in 30-minute timeframes."},
    {"question_number": "Q-10", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "InnerP", "question": "What shots on goal did each team take, in minutes between 60 and 90?", "question_auto": "None", "question_auto_explanation": "None: The provided EVENTS_DATASET does not contain specific information about shots on goal for either team during the specified time frame of 60 to 90 minutes. Therefore, it is not possible to select an appropriate vector search algorithm based on the available data."},
    {"question_number": "Q-11", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "InnerP", "question": "Show details of the goals scored", "question_auto": "None", "question_auto_explanation": "None: The question does not pertain to a vector search algorithm, as it asks for specific details about the goals scored in the match, which are not provided in the given datasets."},
    {"question_number": "Q-12", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "InnerP", "question": "What was the final scoreline of the match, including goals scored by each team and any additional time or penalty shootout results?", "question_auto": "None", "question_auto_explanation": "None: The question does not require a vector search algorithm as it is a straightforward query about the final scoreline of the match. The answer is already provided in the GAME_RESULT section."},
    {"question_number": "Q-13", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "InnerP", "question": "Who scored the goals for each team in which minutes, and what types of goals were they, such as headers or penalties?", "question_auto": "None", "question_auto_explanation": "None: The provided data does not contain specific information about the goal scorers, the minutes they scored in, or the types of goals (e.g., headers, penalties). Therefore, it is not possible to select an appropriate vector search algorithm based on the given information."},
    {"question_number": "Q-14", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "InnerP", "question": "Were there any yellow or red cards issued during the match, specifying the players involved and the reasons for the bookings?", "question_auto": "Cosine", "question_auto_explanation": "Cosine: The question requires identifying specific events related to player actions (yellow or red cards) within the dataset. Cosine similarity is effective for comparing the relevance of text data, such as event descriptions, to find instances of disciplinary actions. The dataset contains detailed descriptions of events, making it suitable for this algorithm to extract relevant information about cards issued."},
    {"question_number": "Q-15", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "Cosine", "question": "Which players were most effective in disrupting the play of the opponent through tackles, interceptions, and defensive actions?", "question_auto": "Cosine", "question_auto_explanation": "Cosine: The cosine similarity algorithm is suitable for this question because it measures the angle between two vectors, which can effectively capture the similarity in the context of player actions and their effectiveness in disrupting the opponent's play. By analyzing the frequency and impact of tackles, interceptions, and defensive actions, we can determine which players had the most significant influence on the game. This approach allows for a nuanced comparison of players' defensive contributions, regardless of the scale of their actions."},
    {"question_number": "Q-16", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "L1", "question": "Identify the players who had the most possession in each half of the match.", "question_auto": "None", "question_auto_explanation": "None: The question requires analyzing possession data, which is not explicitly provided in the EVENTS_DATASET. The dataset contains event descriptions but does not quantify possession for individual players or halves. Therefore, no vector search algorithm can be applied to answer this question accurately."},
    {"question_number": "Q-17", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "L1", "question": "Which team had the highest ball recovery rate in the first and second halves?", "question_auto": "None", "question_auto_explanation": "None: The provided data does not contain specific information about ball recovery rates for either team in the first and second halves. Therefore, it is not possible to determine which team had the highest ball recovery rate based on the available information."},
    {"question_number": "Q-18", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "L1", "question": "How many goal attempts did each team make during the match, and in which minutes were these attempts made?", "question_auto": "None", "question_auto_explanation": "None: The provided EVENTS_DATASET does not contain specific information about goal attempts made by each team during the match. It focuses on passes, carries, and other actions but does not explicitly mention goal attempts or their timings. Therefore, a vector search algorithm cannot be applied to answer the question based on the available data."},
    {"question_number": "Q-19", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "L1", "question": "Which players had the highest success rate in one-on-one duels throughout the match?", "question_auto": "None", "question_auto_explanation": "None: The provided data does not include specific statistics or metrics regarding the success rates of players in one-on-one duels. Therefore, it is not possible to determine which players had the highest success rate based on the available information."},
    {"question_number": "Q-20", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "L1", "question": "Determine the average distance covered by each player and compare it between the two teams.", "question_auto": "None", "question_auto_explanation": "None: The provided data does not contain specific distance metrics for each player, making it impossible to calculate or compare the average distance covered by players from Spain and England. The events dataset focuses on actions taken during the match rather than quantifiable distance metrics."},
    {"question_number": "Q-21", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "L1", "question": "How many fouls were committed by each team in each half?", "question_auto": "None", "question_auto_explanation": "None: The provided data does not contain specific information about the total number of fouls committed by each team in each half. Therefore, it is not possible to determine the fouls using the available information."},
    {"question_number": "Q-22", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "L1", "question": "How many yellow and red cards were given to each team, and at what times?", "question_auto": "None", "question_auto_explanation": "None: No answer found."},
    {"question_number": "Q-23", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "L1", "question": "How many substitutions were made by each team during the match, and at what times?", "question_auto": "None", "question_auto_explanation": "None: The provided datasets do not contain any information regarding substitutions made by either team during the match."},
    {"question_number": "Q-24", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "L1", "question": "Which players received yellow cards, and for what reason?", "question_auto": "None", "question_auto_explanation": "None: The question does not require a vector search algorithm as it can be answered directly from the provided events dataset. The relevant information is that Ollie Watkins of England received a yellow card for committing a foul against Jos\u00e9 Ignacio Fern\u00e1ndez Iglesias of Spain."},
    {"question_number": "Q-25", "add_match_info": "yes", "temperature":0.1, "top_n":10, "search_type": "L1", "question": "How many free kicks did each team receive, and what was the outcome of each free kick?", "question_auto": "None", "question_auto_explanation": "None: No answer found."}
]