Expert Analysis: Ceará vs Red Bull Bragantino
En el enfrentamiento entre Ceará y Red Bull Bragantino, ambos equipos se preparan para un emocionante partido programado para el 16 de agosto de 2025 a las 19:00. Este encuentro es parte de la intensa competencia en el fútbol brasileño, donde la estrategia y la ejecución en el campo serán clave para determinar el resultado. A continuación, se presentan análisis detallados basados en datos de apuestas y estadísticas relevantes.
Ceara
LDWLL
-
Red Bull Bragantino
LLLLL
Date: 2025-08-16
Time: 19:00
Venue: Arena Castelao
Predictions:
| Market | Prediction | Odd | Result |
|---|---|---|---|
| Both Teams Not To Score In 1st Half | 89.10% | 1.15 Make Bet | |
| Over 1.5 Goals | 73.50% | 1.50 Make Bet | |
| Over 0.5 Goals HT | 69.10% | 1.53 Make Bet | |
| Sum of Goals 2 or 3 | 64.80% | 1.95 Make Bet | |
| Under 2.5 Goals | 65.20% | 1.57 Make Bet | |
| Both Teams Not To Score In 2nd Half | 67.40% | 1.25 Make Bet | |
| Over 4.5 Cards | 67.20% | Make Bet | |
| Under 5.5 Cards | 67.30% | Make Bet | |
| First Goal Between Minute 0-29 | 56.50% | 1.83 Make Bet | |
| Away Team Not To Score In 1st Half | 55.80% | Make Bet | |
| Away Team To Score In 2nd Half | 56.40% | Make Bet | |
| Home Team To Score In 2nd Half | 55.00% | Make Bet | |
| Last Goal Minute 0-72 | 53.80% | Make Bet | |
| Home Team Not To Score In 1st Half | 57.60% | Make Bet | |
| Yellow Cards | 3.09% | Make Bet | |
| Avg. Total Goals | 3.11% | Make Bet | |
| Avg. Goals Scored | 2.21% | Make Bet | |
| Avg. Conceded Goals | 1.60% | Make Bet | |
| Red Cards | 0.06% | Make Bet |
Betting Predictions
First Half Insights
- Both Teams Not To Score In 1st Half: 88.20
- Over 1.5 Goals: 73.10
- Over 0.5 Goals HT: 70.40
- Away Team Not To Score In 1st Half: 57.80
- Home Team Not To Score In 1st Half: 55.20
- First Goal Between Minute 0-29: 53.60
Total Match Predictions
- Sum of Goals 2 or 3: 67.70
- Under 2.5 Goals: 64.90
- Over 4.5 Cards: 65.10
- Under 5.5 Cards: 65.60
- Last Goal Minute 0-72: 53.70
Second Half Insights
- Both Teams Not To Score In 2nd Half: 68.20
- Away Team To Score In 2nd Half: 57.40
- Home Team To Score In 2nd Half: 54.90
Average and Additional Statistics
Average Total Goals: 2.21
Average Goals Scored: 1.41
Average Conceded Goals: 1.30
Average Yellow Cards: 2.89
Average Red Cards: 1.16
El partido entre Ceará y Red Bull Bragantino promete ser una batalla táctica, con ambas escuadras buscando explotar las debilidades del rival mientras protegen sus propias áreas débiles. Dada la probabilidad de que ambos equipos no marquen en la primera mitad (88.20), es probable que el partido comience con una fase de estudio mutuo, donde la defensa y el control del mediocampo serán cruciales. Sin embargo, considerando que el promedio de goles totales es de aproximadamente dos (2.21), es razonable esperar que los goles lleguen, especialmente en la segunda mitad cuando los equipos generalmente se vuelven más ofensivos para buscar un resultado favorable. La posibilidad de ver más de un gol (Over 1.5 Goals: 73.10) y un total de goles entre dos o tres (67.70) sugiere que ambos equipos tienen la capacidad ofensiva para capitalizar las oportunidades que surjan durante el juego. Además, con una alta probabilidad de acumulación de tarjetas (Over 4.5 Cards: 65.10), se anticipa un partido intenso con muchas interrupciones que podrían influir en el flujo del juego y en las decisiones tácticas de los entrenadores. En resumen, los aficionados pueden esperar un encuentro cargado de emoción, con momentos clave que probablemente definirán el resultado del partido. [0]: #!/usr/bin/env python [1]: # -*- coding: utf-8 -*- [2]: # Author: Arthur Evstigneev [3]: # Contact: [email protected] [4]: from __future__ import division [5]: import numpy as np [6]: import matplotlib.pyplot as plt [7]: import scipy.io as sio [8]: import sys [9]: from sklearn.metrics import roc_curve, auc [10]: def load_mat(file_name): [11]: data = sio.loadmat(file_name) [12]: return data[‘data’][0][0] [13]: def load_mat_labels(file_name): [14]: data = sio.loadmat(file_name) [15]: return data[‘label’][0][0] [16]: def get_features(data): [17]: feature_vector = [] [18]: for k in range(0, data.shape[-1]): [19]: if k == data.shape[-1] – 1: [20]: feature_vector.append(data[:, :, k]) [21]: else: [22]: feature_vector.append(data[:, :, k] – data[:, :, k + 1]) [23]: return feature_vector [24]: def get_labels(labels): [25]: labels_ = [] [26]: for label in labels: [27]: if label == ‘S’: [28]: labels_.append(0) [29]: elif label == ‘D’: [30]: labels_.append(1) [31]: return np.asarray(labels_) [32]: def calculate_roc(y_true, y_score): [33]: fpr, tpr, thresholds = roc_curve(y_true, y_score) [34]: roc_auc = auc(fpr, tpr) [35]: return fpr, tpr, roc_auc [36]: def plot_roc(fpr, tpr, roc_auc): [37]: lw = 2 [38]: plt.figure() [39]: plt.plot(fpr, [40]: tpr, [41]: color=’darkorange’, [42]: lw=lw, [43]: label=’ROC curve (area = %0.2f)’ % roc_auc) [44]: plt.plot([0, 1], [0, 1], color=’navy’, lw=lw, linestyle=’–‘) [45]: plt.xlim([0.0, 1.0]) [46]: plt.ylim([0.0, 1.05]) [47]: plt.xlabel(‘False Positive Rate’) [48]: plt.ylabel(‘True Positive Rate’) [49]: plt.title(‘Receiver operating characteristic’) [50]: plt.legend(loc=»lower right») ***** Tag Data ***** ID: N3 description: This snippet calculates the ROC curve and AUC score from true labels and predicted scores using sklearn’s roc_curve and auc functions. start line: 32 end line: 35 dependencies: – type: Function name: calculate_roc start line: False context description: This function is critical for evaluating the performance of binary classifiers by computing the ROC curve and AUC score. algorithmic depth: 4 algorithmic depth external: N obscurity: 2 advanced coding concepts: 3 interesting for students: 4 self contained: Y ************* ## Suggestions for complexity 1. **Dynamic Threshold Adjustment**: Modify the function to dynamically adjust the thresholds based on a custom scoring function that takes into account both precision and recall. 2. **Multi-Class ROC Calculation**: Expand the function to handle multi-class classification problems by computing the ROC curve and AUC score for each class and averaging them. 3. **Bootstrapping for Confidence Intervals**: Implement bootstrapping to compute confidence intervals for the ROC curve and AUC score to assess the stability of the classifier’s performance. 4. **Integration with Custom Metrics**: Allow integration with custom evaluation metrics that are specific to certain applications (e.g., cost-sensitive evaluation). 5. **Real-time ROC Update**: Modify the function to update the ROC curve in real-time as new data points come in without recalculating everything from scratch. ## Conversation Hello AI I have a function to calculate ROC curve and AUC but i want to make it more advanced Sure! Could you please share your current implementation so I can understand what you’re working with? here it is [SNIPPET] Great! How would you like to enhance this function? Do you have any specific requirements in mind? yes i want to add dynamic threshold adjustment based on a custom scoring function that considers both precision and recall how can we do that Alright! First we need to define a custom scoring function that balances precision and recall (often called F-beta score). Then we’ll adjust the thresholds dynamically based on this scoring function. Here’s how we can modify your existing function: python from sklearn.metrics import precision_recall_curve def calculate_dynamic_roc(y_true, y_score): precision, recall, thresholds = precision_recall_curve(y_true, y_score) # Define custom scoring function (F-beta score) beta = 1 # You can change beta value as needed fscore = (1 + beta**2) * (precision * recall) / (beta**2 * precision + recall) # Find the threshold that gives the best F-score best_threshold_index = np.argmax(fscore) best_threshold = thresholds[best_threshold_index] # Use this threshold to compute ROC curve and AUC score y_pred = (y_score >= best_threshold).astype(int) fpr, tpr, _ = roc_curve(y_true, y_pred) roc_auc = auc(fpr, tpr) return fpr, tpr, roc_auc # Usage example: # fpr, tpr, roc_auc = calculate_dynamic_roc(y_true_data, y_score_data) This modification includes a custom scoring function based on F-beta score and dynamically adjusts thresholds based on this score before computing the ROC curve and AUC. thanks what if i want to extend this for multi-class classification any ideas Certainly! For multi-class classification problems, you need to compute the ROC curve and AUC score for each class individually and then average them. Here’s how you could extend your function: python from sklearn.preprocessing import label_binarize def calculate_multi_class_roc(y_true, y_score, n_classes): # Binarize the output labels for multi-class computation y_true_bin = label_binarize(y_true, classes=[*range(n_classes)]) fpr = dict() tpr = dict() roc_auc = dict() for i in range(n_classes): fpr[i], tpr[i], _ = roc_curve(y_true_bin[:, i], y_score[:, i]) roc_auc[i] = auc(fpr[i], tpr[i]) # Dynamic threshold adjustment for each class using custom scoring function (F-beta score) precision_i, recall_i, thresholds_i = precision_recall_curve(y_true_bin[:, i], y_score[:, i]) fscore_i = (1 + beta**2) * (precision_i * recall_i) / (beta**2 * precision_i + recall_i) best_threshold_index_i = np.argmax(fscore_i) best_threshold_i = thresholds_i[best_threshold_index_i] # Update scores using best threshold for each class y_pred_i = (y_score[:, i] >= best_threshold_i).astype(int) fpr[i], tpr[i], _ = roc_curve(y_true_bin[:, i], y_pred_i) roc_auc[i] = auc(fpr[i], tpr[i]) # Compute micro-average ROC curve and ROC area fpr[«micro»], tpr[«micro»], _ = roc_curve(y_true_bin.ravel(), y_score.ravel()) roc_auc[«micro»] = auc(fpr[«micro»], tpr[«micro»]) return fpr, tpr, roc_auc # Usage example: # n_classes should be equal to number of unique classes in your dataset. # fpr, tpr, roc_auc = calculate_multi_class_roc(y_true_data_multi_class, # y_score_data_multi_class, # n_classes=number_of_classes) This code handles multi-class classification by computing individual ROC curves and AUC scores for each class using one-vs-all strategy and also computes a micro-average ROC curve. *** Excerpt *** I shall begin my analysis with a detailed look at several passages from Aristotle’s Metaphysics where he explicitly criticizes Plato’s theory of Forms or Ideas as it appears in his dialogue Phaedo.[12] I shall then show how some of these criticisms can be resolved when Plato’s own writings are read closely and carefully. Aristotle’s first objection concerns Plato’s doctrine that Forms are separate substances or beings.[13] Aristotle does not dispute Plato’s argument that Forms are separate from sensible particulars; rather he objects to Plato’s conclusion that Forms are separate substances or beings.[14] Aristotle claims that since sensible particulars participate in Forms they cannot be separate substances or beings.[15] This is because according to Aristotle substances are things which are neither predicated of a subject nor present in a subject.[16] Substances are independent existents which are not predicated of any subject or present in any subject.[17] But since sensible particulars participate in Forms they cannot be independent existents; rather they are present in Forms.[18] Thus sensible particulars are present in a subject; they are not substances but accidents.[19] Aristotle concludes that since sensible particulars are not substances but accidents then Forms cannot be separate substances or beings.[20] For if sensible particulars are accidents then they must have a substance; otherwise they would not be present in anything.[21] Thus since sensible particulars participate in Forms they must have Forms as their substance.[22] *** Revision *** ## Plan To create an exercise that challenges advanced understanding while necessitating additional factual knowledge beyond what is provided in the excerpt itself requires several adjustments: – Integrate specific references to other works by Aristotle and Plato that relate directly to their metaphysical arguments but were not mentioned in the original excerpt. – Include terminology specific to Aristotelian metaphysics such as «ousia» (substance) versus «symbebekos» (accident), which demands familiarity with Aristotle’s categories. – Embed complex logical structures within the argument presented by Aristotle against Plato’s theory of Forms—specifically utilizing nested conditionals («If…then…if…then») and counterfactuals («Had…then…would have»). – Introduce subtle distinctions between different interpretations of «separation» as it applies to Platonic Forms versus Aristotelian substances. ## Rewritten Excerpt In undertaking an analysis rooted deeply within Aristotelian critique of Platonic metaphysics—specifically through an examination of selected passages within Aristotle’s Metaphysics—it becomes evident that Aristotle levies substantial objections against Plato’s exposition on the theory of Forms as delineated within his dialogue Phaedo.[12] My endeavor will be to elucidate upon these criticisms whilst also proposing resolutions by engaging closely with Plato’s corpus. Aristotle inaugurates his critique with an objection against Plato’s assertion regarding the separateness of Forms as distinct entities or substances.[13] It is not Aristotle’s intention to refute Plato’s premise concerning the distinctness of Forms from sensible particulars; rather his contention lies with Plato’s deduction positing these Forms as autonomous entities or beings.[14] Aristotle posits that given sensible particulars’ participation in Forms precludes their status as independent entities; they inherently reside within Forms.[15] According to Aristotelian ontology—wherein substances (“ousiai”) represent entities neither predicated upon nor contained within another subject—such participatory interaction contradicts their independence.[16][17] Consequently, sensible particulars manifest not as independent entities but rather as contingent attributes (“symbebekota”), embedded within a substrate.[18] This line of reasoning leads Aristotle to deduce that if sensible particulars assume the role of contingent attributes rather than independent entities (“ousiai”), then it logically follows that Forms cannot embody separateness akin to substances or autonomous beings.[19][20] This deduction stems from an underlying principle within Aristotelian metaphysics—that contingent attributes necessitate an underlying substance for their existence; absent such a substrate, their presence is unfounded.[21] Henceforth, if sensible particulars indeed engage with Forms through participation—a premise accepted by both philosophers—it necessitates acknowledging these Forms as foundational substances underpinning said participation.[22] ## Suggested Exercise In Aristotle’s critique of Plato’s theory of Forms as presented above: Aristotle argues against Plato’s conclusion regarding the separateness of Forms by asserting that since sensible particulars partake in these Forms—thereby making them contingent attributes rather than independent entities—Forms themselves cannot be considered separate substances or autonomous beings due to their necessary role as underlying substances for these attributes. Given this context: Which statement most accurately captures the essence of Aristotle’s criticism against Plato’s theory of Forms? A) Aristotle disputes Plato’s premise on the distinctness between sensible particulars and Forms but concurs with his conclusion about their separateness as autonomous entities. B) Aristotle agrees with Plato on both premises regarding the distinctness between sensible particulars and Forms and their consequent separateness as autonomous entities. C) While accepting Plato’s premise on the distinctness between sensible particulars and Forms, Aristotle disputes Plato’s conclusion about their separateness by arguing that this participatory relationship renders sensible particulars contingent attributes dependent on Forms as underlying substances. D) Aristotle refutes both Plato’s premise on the distinctness between