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Girabola stats & predictions

Introduction to Girabola Angola

Girabola Angola, officially known as the Girabola, is the premier football league in Angola. It features a fierce competition among some of the top clubs in the country, drawing attention from football enthusiasts worldwide. This league is not only a platform for showcasing local talent but also serves as a battleground for expert betting predictions and analysis. With daily updates on fresh matches, fans and bettors alike can stay informed about the latest developments and strategic insights.

Angola

Girabola

Understanding Girabola Angola

The Girabola consists of 16 teams that compete throughout the season. The league operates on a double round-robin format, ensuring each team plays against every other team twice—once at home and once away. This structure provides ample opportunities for thrilling matches and unexpected outcomes, making it a favorite among football analysts and bettors.

Top Teams to Watch

Several clubs have consistently dominated the Girabola, including Petro de Luanda, Primeiro de Agosto, and Recreativo do Libolo. These teams have a rich history of success and are often at the forefront of the league standings. However, the dynamic nature of football means that underdogs can rise to challenge these giants, adding an element of unpredictability to each season.

Expert Betting Predictions

Betting on Girabola matches requires a deep understanding of team dynamics, player form, and historical performance. Expert analysts provide daily predictions to help bettors make informed decisions. These predictions consider various factors such as head-to-head statistics, recent form, injuries, and even weather conditions that could influence match outcomes.

Factors Influencing Match Outcomes

  • Team Form: Analyzing recent performances can provide insights into a team's current momentum.
  • Head-to-Head Records: Historical matchups can indicate potential advantages or challenges.
  • Injuries and Suspensions: Key player absences can significantly impact a team's strategy.
  • Home Advantage: Teams often perform better on familiar grounds.
  • Climatic Conditions: Weather can affect play styles and outcomes.

Daily Match Updates

Staying updated with daily match results is crucial for anyone involved in betting or following the league closely. Our platform provides real-time updates on all Girabola matches, ensuring you never miss a moment of action. From goal scorers to red cards, every detail is covered comprehensively.

How to Use Betting Predictions Effectively

To maximize your betting potential, it's essential to combine expert predictions with your analysis. Here are some tips:

  • Diversify Your Bets: Spread your bets across different outcomes to manage risk.
  • Analyze Trends: Look for patterns in team performances over multiple matches.
  • Stay Informed: Keep up with the latest news and updates about teams and players.
  • Set Limits: Establish a budget for betting to avoid financial strain.

The Role of Analytics in Football Betting

Advanced analytics play a significant role in modern football betting. By leveraging data-driven insights, bettors can gain an edge over traditional methods. Analytics can reveal hidden patterns and trends that are not immediately apparent through casual observation.

Data-Driven Insights

Data analytics tools analyze vast amounts of information to provide actionable insights. These tools consider factors such as possession statistics, passing accuracy, and defensive solidity to predict match outcomes with higher accuracy.

The Importance of Live Streaming

Live streaming services offer fans the opportunity to watch Girabola matches in real-time from anywhere in the world. This accessibility enhances the viewing experience and allows bettors to react promptly to live events during matches.

Tips for Watching Matches Live

  • Select Reliable Streaming Services: Choose platforms known for high-quality streams and minimal interruptions.
  • Create Alerts: Set up notifications for key moments like goals or substitutions.
  • Analyze During Halftime: Use breaks to assess team strategies and adjust predictions accordingly.
  • Engage with Community Discussions: Join forums or social media groups to share insights and opinions with fellow fans.

The Future of Girabola Angola

The Girabola continues to evolve with advancements in technology and changes in football tactics. The integration of VAR (Video Assistant Referee) has brought more accuracy to officiating, while investments in youth development are nurturing future stars. As the league grows in popularity, it attracts more international attention, promising exciting developments ahead.

Youth Development and Scouting

Girabola clubs are increasingly focusing on youth academies to develop local talent. Scouting networks have expanded beyond Angola's borders, identifying promising players who can contribute to the league's competitive spirit. This emphasis on youth development ensures a steady influx of fresh talent into the professional ranks.

Sponsorships and Financial Growth

Sponsorship deals play a crucial role in the financial health of Girabola clubs. Partnerships with local businesses and international brands provide essential funding for infrastructure improvements, player salaries, and marketing efforts. As the league gains more visibility, sponsorship opportunities are expected to increase, further boosting its growth trajectory.

Cultural Impact of Football in Angola

Football is more than just a sport in Angola; it is an integral part of the cultural fabric. The passion for football unites communities across the country, fostering a sense of identity and pride. Major matches are celebrated events that bring people together, highlighting the sport's role in promoting social cohesion.

Fans' Role in Shaping the League's Identity

tiaojiaozi/Self-Driving-Car-Traffic-Sign-Classifier<|file_sep|>/Traffic_Sign_Classifier.md ## Project: Build a Traffic Sign Recognition Classifier [//]: # (Image References) [image1]: ./images/german_traffic_signs.png "German Traffic Signs" [image1a]: ./images/german_traffic_signs_3.png "German Traffic Signs" [image1b]: ./images/german_traffic_signs_4.png "German Traffic Signs" [image1c]: ./images/german_traffic_signs_5.png "German Traffic Signs" [image1d]: ./images/german_traffic_signs_6.png "German Traffic Signs" [image1e]: ./images/german_traffic_signs_7.png "German Traffic Signs" [image1f]: ./images/german_traffic_signs_8.png "German Traffic Signs" [image1g]: ./images/german_traffic_signs_9.png "German Traffic Signs" [image4]: ./images/signs_0.png "Traffic Sign Example" [image5]: ./images/signs_10.png "Traffic Sign Example" [image6]: ./images/signs_11.png "Traffic Sign Example" [image7]: ./images/signs_12.png "Traffic Sign Example" [image8]: ./images/signs_13.png "Traffic Sign Example" [image9]: ./images/signs_14.png "Traffic Sign Example" [image10]: ./images/signs_15.png "Traffic Sign Example" [image11]: ./images/pipeline_image_0.png "Pipeline Image Example" [image12]: ./images/pipeline_image_1.png "Pipeline Image Example" [video1]: ./output_images/test_video_output.mp4 "Test Video" --- ### Writeup / README #### Allowing your car to drive itself requires that it makes safe decisions based on traffic signs around it. In this project I used convolutional neural networks (CNN) algorithm from Keras library which has built-in architecture for CNN training. In this writeup I will discuss: * [The architecture](#the-architecture) * [Training process](#training-process) * [Testing on new images](#testing-on-new-images) * [Reflection](#reflection) ### Dataset Summary & Exploration #### Visualize first six images from training set The pickled data is provided by Udacity - [here](https://d17h27t6h515a5.cloudfront.net/topher/2016/October/580d53ce_traffic-sign-data/traffic-sign-data.zip). It contains features as well as labels. The pickled data contains: * features: - image data: A tensor with shape `(number_of_examples, width_in_pixels,height_in_pixels,color_channels)` - `features.shape = (34799 ,32 ,32 ,3)` * labels: - label data: A list with shape `(number_of_examples,)` - `labels.shape = (34799,)` #### Visualize histogram Here is an exploratory visualization of the data set. ![alt text][image1] The above image shows how training dataset is distributed among different classes. I noticed that there are lots of classes which has very less examples compared other classes. ![alt text][image1a] ![alt text][image1b] ![alt text][image1c] ![alt text][image1d] ![alt text][image1e] ![alt text][image1f] ![alt text][image1g] To visualize images from each class I wrote below function which takes label number as input parameter. python def show_images_by_label(label_number): rows = [] rows.append([plt.subplot(3,4,i+1) for i in range(12)]) sign_labels = pd.Series(features).value_counts() print(sign_labels) count =0 for i,row in enumerate(rows): count +=1 if sign_labels[label_number] >= count: plt.title(sign_labels.index[label_number]) plt.imshow(features[labels == label_number][count-1]) Using above function I visualized some examples from each class. python show_images_by_label(0) show_images_by_label(10) show_images_by_label(11) show_images_by_label(12) show_images_by_label(13) show_images_by_label(14) show_images_by_label(15) And here are examples from above calls: ![alt text][image4] ![alt text][image5] ![alt text][image6] ![alt text][image7] ![alt text][image8] ![alt text][image9] ![alt text][image10] ### Designing network architecture #### Preprocessing steps As we can see from above histogram that training dataset has unbalanced classes. So before feeding this data into CNN I decided to balance number of examples per class. To achieve this I created below function which takes max number per class as input parameter. python def balance_classes(max_number): new_features = [] new_labels = [] sign_labels = pd.Series(features).value_counts() max_class = sign_labels.idxmax() max_count = sign_labels.max() print("Max Class",max_class," Max Count ",max_count) for i,label_count in enumerate(sign_labels): if label_count >= max_number: samples_to_add = max_number - label_count selected_features = np.random.choice(np.where(labels == i)[0],samples_to_add) new_features.extend(features[selected_features]) new_labels.extend(labels[selected_features]) else: new_features.extend(features[labels == i]) new_labels.extend(labels[labels == i]) Using above function I balanced my dataset by increasing examples per class up to maximum found class count which was around **2000** examples. So now all classes have **2000** examples each. So after balancing dataset size became around **40000** samples. Now before feeding data into CNN model I applied some preprocessing techniques: * Convert images from RGB format into grayscale format. * Normalize pixel values between [-1,+1] range. * Apply Gaussian smoothing filter over grayscale images. Here is an example after applying above preprocessing steps: Before: ![alt text][image4] After: #### Network architecture For this project I used pre-trained CNN model available inside Keras library called **LeNet**. LeNet architecture consists following layers: * Convolutional layer (with kernel size **5x5**). * Max pooling layer (with pool size **2x2**). * Convolutional layer (with kernel size **5x5**). * Max pooling layer (with pool size **2x2**). * Flatten layer. * Fully connected layer (with neurons count **120**). * Fully connected layer (with neurons count **84**). * Fully connected layer (with neurons count **43**, one per class). Here is my final model architecture: | Layer | Description | |:---------------------:|:---------------------------------------------:| | Input | 32x32x3 RGB image | | Convolutional Layer | Filter: **5x5**, Stride: **1**, Activation: RELU | | Max pooling | Filter: **2x2**, Stride: **2** | | Convolutional Layer | Filter: **5x5**, Stride: **1**, Activation: RELU | | Max pooling | Filter: **2x2**, Stride: **2** | | Flatten Layer | Fully connected layer | | Fully connected Layer | Neurons Count: **120**, Activation Function: RELU | | Fully connected Layer | Neurons Count: **84**, Activation Function: RELU | | Fully connected Layer | Neurons Count: **43**, Activation Function: Softmax| #### Model Training To train model I used Adam optimizer with learning rate set as `0.001` . I trained model using batch size `64` over `30` epochs. Validation accuracy after training was `98%`. ### Testing on New Images Here are six German traffic signs that I found on web: Here are results after running prediction on above images: My model was able to correctly guess five out of six traffic signs,(Accuracy = `83%`), which is not bad considering fact that these images were taken by me using my phone camera under different lighting conditions. The only image my model failed to predict correctly was last one which was showing speed limit sign with value `80`. Model predicted this sign as speed limit `120`. Here are some reasons why my model failed: * The quality of image was not good enough so after applying preprocessing steps some information got lost. * Model was not trained on large number of images taken under different lighting conditions. * Model did not see any example similar enough so it could have guessed correct class. ### Video Implementation Here's a [link](./output_images/test_video_output.mp4) to my video result. ### Reflection #### At first I tried LeNet architecture without any preprocessing techniques or balancing dataset classes. As you can see below I achieved validation accuracy `95%` but test set accuracy was very low around `45%`. After few attempts I figured out why my model did not generalize well. It seems like model learned how distribution looks like rather than learning how individual classes look like. This happened because some classes had significantly larger number of examples compared other classes. So my model learned how distribution looks like instead learning individual classes. After balancing dataset classes I achieved validation accuracy `98%` but test set accuracy was still low around `60%`. This time I figured out why my model did not generalize well. It seems like when applying preprocessing techniques such as grayscale conversion or normalization some information got lost so when running prediction on test set some signs were misclassified. #### What would you search for next? One thing that might help improve my model performance would be increasing number of samples per class even further because currently all classes have same number of samples which may lead situation where CNN will be confused between similar looking classes. Another thing would be using transfer learning where we would take pre-trained model such as VGG16 or ResNet50 instead using LeNet architecture because these models were trained on millions of images which should help improve performance.<|repo_name|>tiaojiaozi/Self-Driving-Car-Traffic-Sign-Classifier<|file_sep|>/writeup_template.md # **Traffic Sign Recognition** ## Writeup Template ### You can use this file as a template for your writeup if you want to submit it as a markdown file, but feel free to use some other method and submit a pdf if you prefer. --- **Build a Traffic Sign Recognition Project** The goals / steps of this project are the following: * Load the data set (see below for links to the project data set) * Explore, summarize and visualize the data set * Design, train and test a model architecture * Use the model to make predictions on new images * Analyze the softmax probabilities of the new images * Summarize the results with a written report [//]: # (Image References) [image1]: ./examples/visualization.jpg "Visualization" [image2]: ./examples/grayscale.jpg "Grayscaling" [image3]: ../examples/random_noise.jpg "Random Noise" [image4]: ../examples/placeholder.png "Traffic Sign #1" [image5]: ../examples/placeholder.png "Traffic Sign #2" [image