<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.10.0">Jekyll</generator><link href="/feed.xml" rel="self" type="application/atom+xml" /><link href="/" rel="alternate" type="text/html" /><updated>2025-12-06T21:40:25+00:00</updated><id>/feed.xml</id><title type="html">Blog at Jason</title><subtitle>Always exploring</subtitle><entry><title type="html">Beyond the Box Score: Building a Modern Analytics Platform for the 2025 Jones Cup</title><link href="/posts/Beyond-the-Box-Score-Building-a-Modern-Analytics-Platform-for-the-2025-Jones-Cup" rel="alternate" type="text/html" title="Beyond the Box Score: Building a Modern Analytics Platform for the 2025 Jones Cup" /><published>2025-07-30T04:31:40+00:00</published><updated>2025-07-30T04:31:40+00:00</updated><id>/posts/Beyond-the-Box-Score:-Building-a-Modern-Analytics-Platform-for-the-2025-Jones-Cup</id><content type="html" xml:base="/posts/Beyond-the-Box-Score-Building-a-Modern-Analytics-Platform-for-the-2025-Jones-Cup"><![CDATA[<h4 id="the-why-a-need-for-deeper-insight">The “Why”: A Need for Deeper Insight</h4>

<p>After getting the incredible opportunity to be the attaché for the Taiwan National A team, I knew I wanted to contribute in a unique way. As a long-time follower of the Jones Cup, I was aware of a gap: the <a href="https://jonescup.meetagile.com/">official stats</a> were often limited to basic box scores. For coaches and scouts trying to gain a competitive edge, this wasn’t enough. It’s hard to understand a team’s true strengths and weaknesses at a glance without clear visualizations and the advanced metrics that have become standard in modern basketball.</p>

<p>This gap became my project. I decided to create a full-stack web application dedicated to providing accessible, visualized, and insightful analytics for the tournament.</p>

<h4 id="the-what-an-interactive-scouting-hub">The “What”: An Interactive Scouting Hub</h4>

<p>The result is the <a href="https://jonescup.web.app/">Jones Cup Basketball Analytics</a> website. It’s a centralized hub designed for coaches, scouts, and fans to explore the tournament’s statistical landscape. The platform is built around a few core features:</p>

<ul>
  <li>
    <p><strong>Team Efficiency Landscape:</strong> Instead of just seeing points and rebounds, you can immediately assess a team’s strategic identity. This dashboard visualizes key advanced metrics like <strong>Pace</strong> (Possessions per 40 minutes), <strong>Offensive Rating</strong> (Points per 100 possessions), and <strong>Defensive Rating</strong>. It also breaks down Dean Oliver’s <strong>Four Factors</strong> (Shooting, Turnovers, Rebounding, and Free Throws) into clear, comparative charts, allowing for quick analysis of how teams win games.</p>
  </li>
  <li>
    <p><strong>Interactive Player Stat Explorer:</strong> This is the core of the platform. It allows for deep dives into player performance across the entire tournament. Users can filter by team and position, and most importantly, create their own scatter plots by selecting different metrics for the X and Y axes. Want to see which high-usage players are also the most efficient? Or which players get the most steals relative to their minutes played? This tool makes it possible in seconds.</p>
  </li>
  <li>
    <p><strong>Shot Attempt Charts &amp; Offensive Engine:</strong> To quickly understand a team’s offensive style, the site includes shot distribution charts. It also features an “Offensive Engine” module that immediately highlights the top three players on any given team by Usage Percentage, showing who the offense truly runs through.</p>
  </li>
</ul>

<h4 id="the-how-a-modern-efficient-architecture">The “How”: A Modern, Efficient Architecture</h4>

<p>Building a “full-stack” website for a two-week tournament presented a unique challenge. A traditional, database-heavy approach would be too slow to develop and overkill for the short-term nature of the event. Instead, I opted for a modern, serverless, and highly efficient workflow.</p>

<p>The main design is a <strong>“Static First”</strong> approach. The backend is not a traditional server that’s always running. Instead, it’s a series of automated scripts.</p>

<ol>
  <li>
    <p><strong>Data Extraction (The Backend):</strong> A Python script, which you can see in the <a href="https://github.com/jhsu12/jones_cup_backend">GitHub repository</a>, runs on a schedule. It calls several of the Jones Cup’s internal API endpoints to gather all the raw data for teams, players, and game statistics.</p>
  </li>
  <li>
    <p><strong>Transformation &amp; Calculation:</strong> Once the raw data is fetched, the script cleans it, organizes it, and—most importantly—calculates all the advanced metrics. This includes everything from Usage Rate (USG) to True Shooting Percentage (TS%) and team-level stats.</p>
  </li>
  <li>
    <p><strong>Loading (The “Database”):</strong> Instead of loading this data into a traditional database, the script saves the processed data as a series of optimized JSON files. These files act as a static, pre-computed database that is then uploaded to a cloud storage bucket.</p>
  </li>
  <li>
    <p><strong>The Frontend &amp; Visualizations (D3.js):</strong> The website’s frontend is built with a focus on powerful, custom data visualization. Instead of using a large framework, it leverages the D3.js library to directly manipulate data and draw the complex, interactive charts that are central to the platform. When a user visits, the site fetches the small, pre-computed JSON files, and D3.js goes to work, creating the dynamic scatter plots, bar charts, and shot distribution maps. This approach provides complete control over the visualizations, making them both fast and highly tailored to the specific needs of basketball scouting. The website is a fast, modern single-page application. When a user visits, it doesn’t query a slow database. It simply fetches the small, pre-computed JSON files. This makes loading data, filtering, and creating visualizations incredibly fast for the end-user.</p>
  </li>
</ol>

<p>To ensure the data remained current throughout the tournament, the entire process was automated. I set up a Cloud Scheduler to trigger the Python script every night after all the day’s games were finished. This automation would automatically fetch the new game data, recalculate all the tournament-long statistics, and upload the updated JSON files to Cloud Storage. This meant that every morning, coaches and fans would wake up to a fully updated website reflecting the latest results without any manual intervention.</p>

<h4 id="the-future-integrating-ai">The Future: Integrating AI</h4>

<p>This platform has built a strong foundation, but the work isn’t done. The next phase of this project is to integrate AI to make these analytics even more powerful and intuitive.</p>

<ul>
  <li><strong>Short-Term Goal: Conversational Data Analysis.</strong> The next feature I plan to add is an AI-powered chatbox. This will allow coaches to ask complex questions in natural language and get immediate answers. For example, instead of manually filtering data, a coach could simply ask, <em>“Who has the highest 3-point percentage among all players with more than 50 three-point attempts?”</em> The AI would understand the query, process the data, and provide a direct answer instantly.</li>
</ul>

<p><strong>UPDATE: I have successfully create the first version of the chatbox. It is a simple Q&amp;A system that can answer questions about the tournament, teams, and players. It is currently in the works and will be added to the website in the near future. (Here is the <a href="https://www.youtube.com/watch?v=QdP43NTIMro">demo</a>)</strong></p>

<ul>
  <li>
    <p><strong>Mid-Term Goal: Automated Shot Tracking.</strong> The next major step is to create an AI model that can analyze game footage. This model would be trained to automatically detect and record the on-court position of every shot taken by every player, along with whether it was a make or a miss. This would unlock a new dimension of analysis, automatically generating the kind of detailed shot charts and player heatmaps that are currently only available at the highest levels of professional basketball.</p>
  </li>
  <li>
    <p><strong>Long-Term Goal: Real-Time Strategic AI.</strong> The ultimate vision for this platform is to evolve it into an in-game decision-making tool for coaches. By feeding live game data into a sophisticated AI model, the system could provide real-time suggestions for things like player substitutions based on fatigue and efficiency, identify opponent defensive schemes as they happen, and recommend adjustments to the game plan on the fly. This would transform the tool from a post-game analysis platform into a true digital assistant on the sidelines.</p>
  </li>
</ul>

<p>This project was a fantastic blend of my passions for basketball and technology. I hope it proves to be a valuable tool for the teams involved and for any fan who wants to look beyond the box score.</p>]]></content><author><name></name></author><category term="Basketball" /><summary type="html"><![CDATA[The “Why”: A Need for Deeper Insight]]></summary></entry><entry><title type="html">2025 William Jones Cup Blog</title><link href="/posts/William-Jones-Cup-Blog" rel="alternate" type="text/html" title="2025 William Jones Cup Blog" /><published>2025-07-27T04:31:40+00:00</published><updated>2025-07-27T04:31:40+00:00</updated><id>/posts/William-Jones-Cup-Blog</id><content type="html" xml:base="/posts/William-Jones-Cup-Blog"><![CDATA[<h1 id="-2025-william-jones-cup">🏀 2025 William Jones Cup</h1>

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      <td style="text-align: center"><em>Our Uniform</em></td>
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<p><em>(Just jotting down thoughts, nothing polished haha 😅)</em></p>

<p>At first, I was assigned to the <strong>Australia team</strong>, but after a bit of back-and-forth I ended up with the <strong>Taiwan National Team</strong>. Honestly, I was pretty happy about it—being able to work with the main squad and experience everything from <strong>practices, pre-game prep, games, and post-game routines</strong> all together was a rare and valuable experience.</p>

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      <td style="text-align: center"><em>Gonna go to the stadium</em></td>
    </tr>
  </tbody>
</table>

<p>Since it was the <strong>Taiwan National Team</strong>, there was naturally more work compared to other teams—things like <strong>laundry, towels, dinner boxes</strong>. I was the one coordinating those. I mean, I couldn’t exactly ask the captain 劉錚 to go collect towels 😂.</p>

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      <td style="text-align: center"><em>My everyday job</em></td>
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</table>

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      <td style="text-align: center"><em>The huge laundry bag for the whole team, at least 20kg i think…</em></td>
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</table>

<p>But I was fine with it—<strong>every role is important</strong> in making a team function, and that’s what makes a team great. On top of that, I somehow became <strong>Coach Tucci’s “head ticket manager.”</strong> Whenever he suddenly shouted, <em>“I need 20 tickets!”</em>, I had to make it happen 😅. The man has a LOT of friends haha.</p>

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      <td style="text-align: center"><em>Coach Tucci asking me where are his friends</em></td>
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</table>

<p>Honestly though, the coaching staff was <strong>incredibly tough</strong>. Sometimes we wouldn’t get back to the hotel until 11pm after a game, and they’d still have to hold meetings. The next day, practice might start as early as 9am. One time Coach Tucci sent the game plan to our group chat at <strong>5 in the morning</strong>—I thought to myself:</p>

<blockquote>
  <p><em>“Does this man ever sleep?”</em></p>
</blockquote>

<p>It was intense, but it also showed just how much <strong>passion and dedication</strong> they had.</p>

<p>As for players, my personal favorite throughout the tournament was <strong>阿拉薩</strong>. I know that sounds random, but he was super outgoing and always came up to chat with me. One time at breakfast in the hotel, he just sat right across from me. Really kind guy, and surprisingly good at Chinese too haha.</p>

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      <td style="text-align: center"><em>My guy asks me to help him warm up</em></td>
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<hr />

<h2 id="-two-small-stories">📖 Two Small Stories</h2>

<h3 id="-the-case-of-the-missing-towels">🧵 The Case of the Missing Towels</h3>
<p>Because it was the <strong>national team</strong>, the organizers provided sponsor towels for players to use during games—<strong>10 each time</strong>. After games, I had to collect them all and send them back to the hotel laundry.</p>

<p>One day, after the game, I counted the towels and realized <strong>one was missing</strong>. I checked the bench—nothing. Locker room—nothing. Even asked in the group chat—no response. Weird, since those towels are huge.</p>

<p>The next game, I only had <strong>9 towels</strong>. I kept counting during the game: halftime—still 9, fourth quarter—still 9. After the buzzer, I quickly grabbed 8 towels and stuffed them into the bag. One was left—<strong>Robert</strong> had it on his shoulders. But since he had a post-game interview, I thought, “Okay, no rush, I’ll get it after.”</p>

<p>After his interview, I saw him walking back… but the towel had disappeared. I asked,</p>

<blockquote>
  <p>“Hey bro, where’s the towel? Weren’t you wearing it to the interview?”</p>
</blockquote>

<p>Robert replied,</p>
<blockquote>
  <p>“Oh, I tossed it into the stands for the fans.”</p>
</blockquote>

<p>I was stunned. Then I asked,</p>
<blockquote>
  <p>“Wait… did you also throw one yesterday?”</p>
</blockquote>

<p>He laughed,</p>
<blockquote>
  <p>“Yeah.”</p>
</blockquote>

<p>Directly caught in the act 😅. Later on, when getting off the bus, he even told his family he “accidentally” threw the towel to the fans. His parents looked shocked too haha. Thankfully, it wasn’t a hotel towel—otherwise we’d be paying for it.</p>

<hr />

<h3 id="-big-b-almost-couldnt-play">👕 Big B Almost Couldn’t Play</h3>
<p>One day after laundry was already sent out, I suddenly got a Line message:</p>

<blockquote>
  <p>“Hey Jason, can I still send my clothes to laundry? I forgot earlier.”</p>
</blockquote>

<p>I replied,</p>
<blockquote>
  <p>“Too late for today, you’ll have to wait until tomorrow.”</p>
</blockquote>

<p>Big B replied,</p>
<blockquote>
  <p>“Damn… but that’s my jersey for tonight’s game. What do I do?”</p>
</blockquote>

<p>So I became his <strong>personal laundry assistant</strong>—ran out to a quick-wash shop and got it cleaned in a rush. I thought I’d be lonely, but funny enough, I ran into another player there also doing his laundry alone… his name? <strong>Jason Brickman</strong> 😅. Two Jasons, same laundromat, same time—what are the odds?</p>

<p>After it was done, I left the jersey and receipt at Big B’s hotel door. Later at the game, he even gave me <strong>500 bucks as a delivery tip</strong>. Good guy.</p>

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      <td style="text-align: center"><em>Just save Brandon’s game today haha</em></td>
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<p>But just when I thought that problem was solved… a few days later, he messaged me again:</p>

<blockquote>
  <p>“Hey Jason, when will today’s laundry come back?”</p>
</blockquote>

<p>I said,</p>
<blockquote>
  <p>“Not until after tonight’s game.”</p>
</blockquote>

<p>Big B:</p>
<blockquote>
  <p>“Damn… but that’s my jersey for tonight’s game.”</p>
</blockquote>

<p>It was déjà vu, like I had opened Pandora’s box and the exact same conversation was happening again. I quickly asked the admin center if the laundry had already been picked up. <strong>“YES.”</strong></p>

<p>GG. I immediately told the team manager, who looked serious and asked,</p>

<blockquote>
  <p>“Big B is resting today.”</p>
</blockquote>

<p>I double-checked:</p>
<blockquote>
  <p>“So… he’s on rest?”<br />
“<strong>Yes!</strong>”</p>
</blockquote>

<p>Instant relief. I quickly told Big B,</p>
<blockquote>
  <p>“You’re resting today.”</p>
</blockquote>

<p>He replied,</p>
<blockquote>
  <p>“I know.”</p>
</blockquote>

<p>I asked,</p>
<blockquote>
  <p>“Then why were you so desperate for your jersey?”</p>
</blockquote>

<p>He said,</p>
<blockquote>
  <p>“I only knew there was a 50% chance I’d rest.”</p>
</blockquote>

<p>I joked,</p>
<blockquote>
  <p>“You didn’t ‘accidentally’ lose your jersey on purpose just so you could rest, right?”</p>
</blockquote>

<p>We looked at each other and burst out laughing. 🙌 Thanks to God’s grace, the problem was solved.</p>

<hr />

<h2 id="-wrapping-up">🙏 Wrapping Up</h2>
<p>Big thanks to all the <strong>Jones Cup teammates</strong> who made everything run smoothly.</p>

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      <td style="text-align: center"><em>My colleagues</em></td>
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<p>And of course—please support the <strong>Taiwan National Team!</strong> 🇹🇼<br />
The <strong>FIBA Asia Cup</strong> tips off on <strong>August 6</strong>, first game against the <strong>Philippines</strong>.</p>

<p>💪 We’ve got this—let’s go! 🔥</p>

<h2 id="some-pics">Some pics</h2>

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      <td style="text-align: center"><em>Let’s go baby, we’re here to WIN</em></td>
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      <td style="text-align: center"><em>Timeout</em></td>
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      <td style="text-align: center"><em>You gotta utilize every chances to talk about game plan</em></td>
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      <td style="text-align: center"><em>Ex NBA OKC player - Andre Roberson, he’s playing for the Philipines</em></td>
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      <td style="text-align: center"><em>My guy Rhenz Abando</em></td>
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  </tbody>
</table>]]></content><author><name></name></author><category term="Basketball" /><summary type="html"><![CDATA[🏀 2025 William Jones Cup]]></summary></entry><entry><title type="html">2024 William Jones Cup Attache</title><link href="/posts/William-Jones-Cup" rel="alternate" type="text/html" title="2024 William Jones Cup Attache" /><published>2024-07-21T04:31:40+00:00</published><updated>2024-07-21T04:31:40+00:00</updated><id>/posts/William-Jones-Cup</id><content type="html" xml:base="/posts/William-Jones-Cup"><![CDATA[<blockquote>
  <p>It felt unreal.</p>
</blockquote>

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      <td style="text-align: center"><em>Our Uniform</em></td>
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<p>Although it was a bit tiring, experiencing training and competitions with the Chinese Taipei team was an unforgettable memory. I almost have all the tactics memorized 😂; I just need one more jersey to get on the court 🤣.</p>

<p>This was my first time as an attache, something I had never done before. Because of this, I learned a lot, and it’s a very memorable experience 🏀.</p>

<p>Here’s a simple record of my daily schedule over these past few days:</p>

<p>7:30 AM - Wake up</p>

<p>7:50 AM - Slowly head down for breakfast &amp; confirm the arrival of the driver</p>

<p>8:30 AM - Depart for the court to practice</p>

<p>8:35 AM - After arriving at the court, carry towels and water jugs to the lock room &amp; turn on power at the association office to charge the walkie-talkies</p>

<p>8:40 AM - While the players are taping in the lock room, I push the ball cart onto the court and take a few shots to confirm that the hoop and ball are fine (just an excuse of shooting the ball 😂)</p>

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      <td style="text-align: center"><em>O ne of the subjects of the popular Netflix docuseries “Last Chance U”  - Deshaun Highler</em></td>
    </tr>
  </tbody>
</table>

<p>9:00 AM - Players start practice on time</p>

<p>9:00-9:40 AM - Tactical practice for players</p>

<p>9:40-10:00 AM - Shooting practice</p>

<p>By the way, I remember Chris asked me to help him find a ring that he left in the lock room. Sounds pretty expensive though. But, I didn’t find it…</p>

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      <td style="text-align: center"><em>Former NBA Player - Chris McCullough</em></td>
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<p>10:30 AM - Return to the hotel from the court, and confirm tomorrow’s transport and whether we need to borrow the conference room with the coach</p>

<p>10:40 AM - Return the practice towels to headquarters and borrow towels for the afternoon match</p>

<p>11:00 AM - 1:00 PM - Rest, exercise (sometimes), and have lunch</p>

<p>3:00 PM - Arrive at the court on time</p>

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      <th style="text-align: center"><img src="/assets/jones_cup/fans.JPG" alt="captionless image" /></th>
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      <td style="text-align: center"><em>How it looks like on the bus</em></td>
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<p>3:10 PM - Arrive at the court and carry towels and water jugs</p>

<p>3:20 PM - Slowly enter the lock room; Our team arrives at the court really early, indirectly urging those from the previous match to leave quickly</p>

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      <td style="text-align: center"><em>Quick Pic with Rhenz Abando</em></td>
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<p>3:30 PM - Players taping; I’ll go into the lock room to see what’s today pre-game meal, hoping there’re leftovers 😂</p>

<p>4:00 PM - Confirm the player list with the coach</p>

<p>4:00-5:00 PM - Watch the game and chat with attaches</p>

<p>5:00 PM - Game starts</p>

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      <td style="text-align: center"><em>Pretend to be professional</em></td>
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<p>After each quarter, provide box score to the coaching staff</p>

<p>7:00 PM - Match ends (usually not exactly on time), inform the interviewed players to go to the press conference</p>

<p>7:30 PM - Escort the players to the car slowly since some have a lot of fans, and the coach might have to wait too long 😂</p>

<p>8:00 PM - Return to the hotel for dinner &amp; shower</p>

<table>
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      <td style="text-align: center"><em>Adam Hinton went to our room to sign wallpapers for fans</em></td>
    </tr>
  </tbody>
</table>

<p>11:00 PM - Meeting</p>

<p>12:30 AM - Sleep</p>

<p>Fun fact: There are a total of 130 Kings logos in the warm-up area.</p>

<p>Thanks to the coaching staff and players for being very cooperative during the event, so there weren’t many issues to resolve. I also appreciate my fellow reception partners for encouraging and helping each other, and a big thank you to all the administrative staff for their hard work.</p>

<p>You can’t find me though, I’m the one who took the pic. 😂</p>

<table>
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      <td style="text-align: center"><em>Chinese Taipei Team White</em></td>
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  </tbody>
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      <td style="text-align: center"><em>The meeting room</em></td>
    </tr>
  </tbody>
</table>

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      <td style="text-align: center"><em>Badge</em></td>
    </tr>
  </tbody>
</table>]]></content><author><name></name></author><category term="Basketball" /><summary type="html"><![CDATA[It felt unreal.]]></summary></entry><entry><title type="html">我的救命恩人 - 波蘭克拉克夫</title><link href="/posts/%E6%88%91%E7%9A%84%E6%95%91%E5%91%BD%E6%81%A9%E4%BA%BA" rel="alternate" type="text/html" title="我的救命恩人 - 波蘭克拉克夫" /><published>2023-05-30T04:31:40+00:00</published><updated>2023-05-30T04:31:40+00:00</updated><id>/posts/%E6%88%91%E7%9A%84%E6%95%91%E5%91%BD%E6%81%A9%E4%BA%BA</id><content type="html" xml:base="/posts/%E6%88%91%E7%9A%84%E6%95%91%E5%91%BD%E6%81%A9%E4%BA%BA"><![CDATA[<p>坐在公園裡，哪裡都不太想去😂，可能是太累了，可能是有點孤單，算是第一次獨旅，可能太累加還沒習慣，有點想回瑞典，跟著自己熟悉的人相處在熟悉的環境，這有點像是我剛來瑞典的前幾個禮拜，非常想回台灣，想念台灣的一切，食物、家人、朋友與自己熟悉的環境。</p>

<p>算了，記錄一些今天的事，反正閒著也是閒著，早上5.15爬起來，準備參加6.集合的集中營團，我昨天晚上12.多才抵達hostel，實睡肯定不到5小時，第一次睡在8人宿舍房，整個睡起來很不習慣，心裡想著「幹，東西會不會被偷」，護照、錢包都放在枕頭旁邊，要偷要先通過我這關😂，睡得沒有很好，就起床了。</p>

<p>起床之後，尷尬了，「靠邀，我昨天明明就有充電，啊幹，手機怎麼剩30幾%，手錶也是，挫屎了」，我就先到集合地點在想辦法，心裡想著巴士應該有充電器吧，在巴士上面在充電好了，結果巴士來了，充電座沒來，25%，我晚上還要搭flixbus，先把wifi、網路、藍芽，全部關一關，也把亮度調到最低先撐著。</p>

<p>進去集中營，心裡想著，手機都不用就沒事了，要拍照就用gopro ，找到插座就插，導遊先叫我們去廁所，經過廁所的時候，「欸，有插座」，馬上上完，就去插，站在那邊，大家心裡可能想著，「怎麼有亞洲怪人站在那邊」，充到集合時間前幾分鐘就好，很好，多了2-3%，穩的，等到回市區馬上充電，應該可以吧？</p>

<p>看到大家紛紛拿出手機拍照，加上gopro 拍照很慢，我決定翻開覆蓋的一張卡牌「按米摸拍照術」，太陽超大，螢幕超暗，所以我就靠感覺拍，反正鏡頭對到，89不理10，肯定穩的，阿拍著拍著，手機很不爭氣的剩下20%，不行啊！我一定要撐住啊。</p>

<p>準備上巴士回去時，我從車頭的門上車，「欸，司機大哥的座位怎麼好像有充電座，但他好像在充欸？」管他的，反正他們又不會看台灣youtube ，又不認識我😂，問就對了，「Excuse me, I’m wondering that if I could charge my 」，我手拿著充電器，怕司機聽不懂英文，搭配著比手畫腳，話還沒說完，司機就說「yes!」，開心的是，他有另一個USB孔，所以可以充兩台，手機就這樣充了2個小時，到市區時，70%，舒服，然後我就去吃午餐，然後我就坐在這邊了。</p>

<table>
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      <th style="text-align: center"><img src="https://scontent.ftpe8-2.fna.fbcdn.net/v/t39.30808-6/347413613_149020931425307_4509243559238629087_n.jpg?_nc_cat=100&amp;ccb=1-7&amp;_nc_sid=833d8c&amp;_nc_ohc=lTb0EVVA64gQ7kNvgHwamdg&amp;_nc_zt=23&amp;_nc_ht=scontent.ftpe8-2.fna&amp;_nc_gid=ACImNQX3I8Mmiij1qvfYFCv&amp;oh=00_AYAGFNWJa9AR9nBdxBxW2xnVQERIbXIxZeSL-djbrGQ-bA&amp;oe=67293265" alt="captionless image" /></th>
    </tr>
  </thead>
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      <td style="text-align: center"><em>我在等flixbus 時，看到插座馬上插下去，怕flixbus 沒插座，阿不就還好，我那一班flixbus ，還真的沒有。</em></td>
    </tr>
  </tbody>
</table>]]></content><author><name></name></author><category term="Poland" /><category term="Diary" /><summary type="html"><![CDATA[坐在公園裡，哪裡都不太想去😂，可能是太累了，可能是有點孤單，算是第一次獨旅，可能太累加還沒習慣，有點想回瑞典，跟著自己熟悉的人相處在熟悉的環境，這有點像是我剛來瑞典的前幾個禮拜，非常想回台灣，想念台灣的一切，食物、家人、朋友與自己熟悉的環境。]]></summary></entry><entry><title type="html">Who is the best 2-point scorer in the 2021–2022 regular season of Pleague?</title><link href="/posts/Who-is-the-best-2-point-scorer-in-the-2021-2022-regular-season-of-Pleague" rel="alternate" type="text/html" title="Who is the best 2-point scorer in the 2021–2022 regular season of Pleague?" /><published>2022-10-08T04:31:40+00:00</published><updated>2022-10-08T04:31:40+00:00</updated><id>/posts/Who-is-the-best-2-point-scorer-in-the-2021-2022-regular-season-of-Pleague</id><content type="html" xml:base="/posts/Who-is-the-best-2-point-scorer-in-the-2021-2022-regular-season-of-Pleague"><![CDATA[<p>Over the past 21–22 regular season of PLG, 13 players have taken at least 200 2-point shots. This plot shows their total attempts and FG inside the arc.</p>

<p>During this time Sim Bhullar has attempted the most 2-pointers and without any doubt he’s converting over 50% of them, which is one reason to win the import of the year. I mean, who can stop this big fella if he has the ball in the paint?</p>

<p>Look closely, there’re only 3 local players who have attempted over 200 2-pointers and made over 40% of them, which are Youwei, Amigo and Jet.</p>

<p>Both Youwei and Amigo were in the first team of the year. With another additional award, Youwei won the rookie of the year and Amigo won the MVP of the year.</p>

<p>What about Jet? No award for him, but a ring.</p>

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</table>]]></content><author><name></name></author><category term="Basketball" /><category term="PLG" /><summary type="html"><![CDATA[Over the past 21–22 regular season of PLG, 13 players have taken at least 200 2-point shots. This plot shows their total attempts and FG inside the arc.]]></summary></entry><entry><title type="html">鐘聲響起，塔克老師下課了｜Anthony Tucker</title><link href="/posts/%E9%90%98%E8%81%B2%E9%9F%BF%E8%B5%B7%E5%A1%94%E5%85%8B%E8%80%81%E5%B8%AB%E4%B8%8B%E8%AA%B2%E4%BA%86" rel="alternate" type="text/html" title="鐘聲響起，塔克老師下課了｜Anthony Tucker" /><published>2022-02-20T04:31:40+00:00</published><updated>2022-02-20T04:31:40+00:00</updated><id>/posts/%E9%90%98%E8%81%B2%E9%9F%BF%E8%B5%B7%E5%A1%94%E5%85%8B%E8%80%81%E5%B8%AB%E4%B8%8B%E8%AA%B2%E4%BA%86</id><content type="html" xml:base="/posts/%E9%90%98%E8%81%B2%E9%9F%BF%E8%B5%B7%E5%A1%94%E5%85%8B%E8%80%81%E5%B8%AB%E4%B8%8B%E8%AA%B2%E4%BA%86"><![CDATA[<h2 id="擋拆表現">擋拆表現</h2>
<iframe style=" display: block;
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<p>本季與Tucker打擋拆最多次的球員為Benson，共38次，這個結果其實不意外，基本上鋼鐵人洋將都放一大一小，Benson也不是所謂進攻型的球員，他的工作主要是防守與抓籃板，進攻方面，Benson持球單打成功率不高，得分方式主要是二波補進或靠近籃筐的放籃。當Tucker與Benson打擋拆時，以防守球員的角度來說，就是放Benson外線，場均三分命中率為15.38%，就是放Benson投就對了，那當Benson roll in 時，與外線相比是個比較好的機會，但執行效果也不好，根據gc basket的數據，Benson進攻效率最低的是定點投籃為0.6，第二低的就是擋拆為0.9。</p>

<p>本土最常與Tucker打擋拆的人為鄭德維，共16次，G80面對領航猿時有很多Pick &amp; Pop 三分外線的空擋機會，但場均16.67%的三分球命中率，說明三分線並不是鄭德維的強項，使Tucker在打擋拆時兩名防守者可以更專注於Tucker，防堵Tucker的外線投籃，擋拆成功率也不高，而本季Tucker三分球場均27.7%的低迷狀況，在擋拆後的把握度不是太好，三分球3–12，25%，兩分球5–9，55.5%，雖然超過5成，但兩分與三分的總出手只有21次，出手機會很少，擋拆後傳球的部分，隊友8–22，表現尚可。</p>

<p>總結本季Tucker擋拆的執行狀況，我猜測各隊都已經熟悉Tucker的打法，針對性去防守逼迫Tucker傳球，將球交給外線不擅長的內線球員處理，雖然Tucker偶爾有三分空擋機會，但本季尚未找到準心的老師，命中率也不甚理想，使得本季擋拆的威脅性沒有想像中的大，另外鋼鐵人開季前期還在磨合，在體系尚未形成的狀況下，整體團隊配合較少，大多還是持球單打，這也是Tucker命中率不佳的原因之一。</p>

<h2 id="anthony-tucker-與-taylor-braun-比較">Anthony Tucker 與 Taylor Braun 比較</h2>

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<p>上方的圖是針對球員登錄的所有場次之球隊場均效率比較，其中Anthony Tucker 與 Taylor Barun 有配過2次，可以發現雖然Tucker有登錄時，ADR比Braun登錄時低，但只有低一些，而ANR與Braun差距11.04。</p>

<p>Anthony Tucker 由於本季鋼鐵人陣容關係，有嘗試著打2號，但我在剪影片的時候發現他似乎還沒適應，在陣地戰偶爾會發生Tucker 沒有參與進攻的情況，我認為Tucker還是需要球權才有威脅性，但在合理的出手與分享球的拿捏程度是一個困難的課題，對於Tucker來說，被守到的投籃他還是有能力投進，與命中率不佳的空擋球員相比，到底要不要傳？Tucker不是說不傳，是傳了有時候命中率不理想，所以會自己來，但今天說難聽一點，Tucker沒投進的確是要承擔沒把球傳給最好機會的責任，因為進球是好球，自幹沒進就不合理，舉例來說，G36夢想家對國王轟了19顆三分，其中有一顆是姐夫快攻3分線外1步自拔，合理嗎？我覺得不合理，因為是快攻，有機會更靠近籃筐出手，況且三分也不是姐夫的強項，但最後他進球了，進球就是好球。</p>

<p>而Taylor Braun 屬於團隊型球員，該怎麼樣就怎麼樣，有空擋出手，沒機會就傳掉，在團隊配合上，球的流動性比Tucker在場上時還好，雖說場均三分命中率為25%，比Tucker還低，但Braun場均三分出手為5.33次，Tucker為10.8次，多了約ㄧ倍，另外Braun身高201公分， 1234號都可以打，可以組織控球，場均助攻7.88次(2/19)，聯盟第一，種種因素與陣容關係，的確較適合鋼鐵人，況且在陳又瑋越打越好下，1號不在需要依賴Tucker。</p>

<h2 id="鋼鐵人洋將組合效率">鋼鐵人洋將組合效率</h2>

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<p>上方的圖是針對各洋將組合登錄的所有場次之球隊場均效率比較。</p>

<p>年前10場比賽中，Benson與Tucker、Braun各配4次，Tucker與Braun配2次，大部分場次Benson是自動登入，因為就目前陣容來說，中鋒的高度非常重要，所以撇除Tucker與Braun配，Benson配Braun 的ANR 為4.7，配Tucker的ANR為-11.3，相差16，是非常大的差距，從數據面顯現出目前鋼鐵人的洋將組合Benson配Braun，對於團隊表現有較明顯的幫助。在Tucker被釋出後，補進了同樣也叫Anthony的NBA水貨狀元Anthony Bennett，給了鋼鐵人內線更多選擇，另外CBA台將，台灣LBJ周儀翔的回歸，對於鋼鐵人的戰力也提升了不少，只要周儀翔信心找回來，身手不會差太多，最後祝福塔克老師在台中太陽有好的表現。</p>]]></content><author><name></name></author><category term="Basketball" /><category term="PLG" /><summary type="html"><![CDATA[擋拆表現]]></summary></entry><entry><title type="html">The Best Offenses and Defenses | Basketball on Paper Ch3</title><link href="/posts/The-Best-Offenses-and-Defenses" rel="alternate" type="text/html" title="The Best Offenses and Defenses | Basketball on Paper Ch3" /><published>2022-02-15T04:31:40+00:00</published><updated>2022-02-15T04:31:40+00:00</updated><id>/posts/The-Best-Offenses-and-Defenses</id><content type="html" xml:base="/posts/The-Best-Offenses-and-Defenses"><![CDATA[<p>This chapter talks about how to evaluate Offense Rating and Defense Rating throughout different years. There’re lots of things we need to consider, such as rule, talent, and possibly even changes in the length of shorts affected. Dean Oliver thinks that it’s hard to find the greatest team on offense and defense, but we can find consistencies among those teams, patterns that may suggest how to construct other teams or at least how to predict what teams might be great. For simplicity, we evaluate the teams of history against the averages of their times.</p>

<h2 id="new-terms"><strong>New Terms:</strong></h2>

<blockquote>
  <p><strong>Pace:</strong> Possessions per Game</p>

  <p><strong>Scoring Possessions:</strong> FGM + [ 1 — (1-FT%)² ] * FTA * 0.4</p>

  <p><strong>Plays:</strong> FGA + FTA * 0.4 + TOV</p>

  <p><strong>Floor Percentage:</strong> Scoring possessions/ total possessions</p>

  <p><strong>Field Percentage:</strong> FGM/ total possessions — 0.4*FTA</p>

  <p><strong>Play Percentage:</strong> Scoring possessions/ Plays</p>
</blockquote>

<p><strong>Floor Percentage:</strong>Percentage of a team’s possessions on which the team scores at least 1 point</p>

<p><strong>Field Percentage:</strong> Percentage of a team’s non-foul shot possessions on which the team scores a field goal</p>

<p><strong>Play Percentage:</strong> Percentage of a team’s “plays” on which the team scores at least 1 point</p>

<h2 id="difference-between-plays-and-possessions"><strong>Difference between Plays and Possessions</strong></h2>

<p>Let’s say a team miss a shot and get the offensive rebound, it’s still one possession but two plays.</p>

<h2 id="four-aspects-of-a-game"><strong>Four aspects of a game</strong></h2>

<ol>
  <li>Shooting percentage from the field.</li>
  <li>Getting offensive rebounds.</li>
  <li>Committing turnovers.</li>
  <li>Going to the foul line a lot and making the shots.</li>
</ol>

<p>If you aren’t shooting from the field, you better be doing a few of the other three things. If you don’t have the size to get defensive rebounds, you better force turnovers. If you can’t take care of the ball very well, you better get shots up before you turn it over, then go after the boards.</p>

<p><strong>Interesting topics:</strong></p>

<ol>
  <li>
    <p>Do Pace and Rule affect Efficiency?</p>
  </li>
  <li>
    <p>For great teams, were they Star-Driven or System Driven?</p>
  </li>
  <li>
    <p>For great teams, were there any key positions that needed a great player in order for the team to have a great offense or defense?</p>
  </li>
  <li>
    <p>Is there any correlation between great offense and height?</p>
  </li>
</ol>]]></content><author><name></name></author><category term="Basketball" /><summary type="html"><![CDATA[This chapter talks about how to evaluate Offense Rating and Defense Rating throughout different years. There’re lots of things we need to consider, such as rule, talent, and possibly even changes in the length of shorts affected. Dean Oliver thinks that it’s hard to find the greatest team on offense and defense, but we can find consistencies among those teams, patterns that may suggest how to construct other teams or at least how to predict what teams might be great. For simplicity, we evaluate the teams of history against the averages of their times.]]></summary></entry><entry><title type="html">PLG 2021–2022 球季年前 (G01 — G30)各隊分析(下)</title><link href="/posts/PLG-2021-2022-2" rel="alternate" type="text/html" title="PLG 2021–2022 球季年前 (G01 — G30)各隊分析(下)" /><published>2022-02-01T04:31:40+00:00</published><updated>2022-02-01T04:31:40+00:00</updated><id>/posts/PLG-2021-2022-2</id><content type="html" xml:base="/posts/PLG-2021-2022-2"><![CDATA[<p>本篇將針對年前戰績後三名，富邦勇士、高雄鋼鐵人與桃園領航猿做分析，以各隊的進攻效率、防守效率與我看比賽的印象為依據，分享我的看法。</p>

<p>還沒看上篇的請往這：<a href="https://jhsu12.github.io/posts/PLG-2021-2022-1"><strong>PLG 2021–2022 球季年前(G01 — G30)各隊分析(上)</strong></a></p>

<h2 id="四富邦勇士外圍防守再加油">（四）富邦勇士，「外圍防守再加油」</h2>

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<p>PLG元年冠軍富邦勇士這季雖飽受傷兵困擾，但在12場比賽中，整體OR來到100.76分，聯盟第二，看似不錯，但以折線圖來看，得分效率似乎不是太穩定，最高效率與最低效率都是在捷運大戰面對到新北國王，兩場最大差距就是在三分球命中率，神仙大戰那場三分球14–41，34.1%，而另一場4–35，11.4%，球隊場均三分球命中率為28.98%，聯盟第四，這也與傷兵人數有了直接的關係，翻開上季勇士球員表現，共有五位球員在場均出手至少3次下，三分球命中率超過三成，分別有Michael Singletary、簡偉儒、林書緯、張宗憲與 Big Z，而本季只有兩位球員在場均出手至少3次下，三分球命中率超過三成，分別是Michael Singletary與 Big Z，雖然傷兵陸續回來，但對於勇士來說，這些球員需要花多少時間調整就是個未知數，況且勇士第一得分手Singletary目前也進入到了傷兵名單，年後Singletary是否能健康的出賽，對於勇士極其重要。</p>

<p>防守方面，DR來到了100.5分，聯盟倒數第一，這讓我很好奇，本季找來230cm專剋Bhullar的 Samuel Deguara助陣，同樣擁有超過225cm Bhullar的攻城獅，為何攻城獅DR第一，勇士第六，比較了兩人場均表現，Bhullar 場均25分21.8籃板2.7火鍋3.6失誤1.7犯規，而Deguara場均15.6分10籃板0.6火鍋3.4失誤3.8犯規，除了場均失誤次數小贏Bhullar，其他明顯都差Bhullar一大截，與隊內Big Z場均19.11分10.44籃板1.22火鍋2.11失誤3犯規，相比下似乎Big Ｚ表現較好，Deguara不管是在進攻或防守都沒有達到理想中的統治禁區，所以勇士大多還是以Big Z配Singletary為主。那勇士問題到底出在哪？抓了各隊場均對手的2分命中率與3分命中率，勇士場均對手2分命中率為47.88%，為聯盟第三低的，表現中段班，但場均對手3分命中率為34.26%，聯盟六隊最高，這說明勇士在3分線外的防守出了ㄧ些問題，如何有效降低對手的外圍投射命中率，減少失分，是目前勇士需要解決的問題。</p>

<h2 id="五高雄鋼鐵人本季黑馬其他球隊要小心了">（五）高雄鋼鐵人，「本季黑馬？其他球隊要小心了」</h2>

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<p>另一隻新成軍的球隊高雄鋼鐵人，在10場比賽中OR來到95.8分，聯盟第四，對於很多人來說應該非常意外，因為球員組成大多是以新秀為主，不過從熱身賽的表現可以知道，本土球員其實表現還不錯，尤其是隊長男模，在面對勇士一役砍下35分帶隊贏球，這一勝對於球隊非常重要，為開季增添許多信心，但在Coach Berry 接掌兵服後，似乎還在熟悉本土球員使用方式，新秀與洋將也在適應開季的強度與體系，在開季前6場比賽OR較不穩定，但從數據來看，年前最後4場比賽OR有上升的趨勢，進攻狀況非常好，我想這與體系的建立與球員的成長有著直接的關係。</p>

<p>新秀部分，陳又瑋的組織日漸成熟，定位也較明確，藍少甫長出了三分，場均三分命中率33.3%，與郭少傑、成力煥不相上下，旅美球員王律翔，雖在第二次面對夢想家時，錯失許多三分空擋，三分球 1–6，16.7%，但在年前倒數兩場比賽，三分球4–8，50%的高命中率，把握度高，是球隊近來2連勝的關鍵之一，另外洋將部分，目前應該會持續擺上Talyor Braun與Keith Benson，上季的Tucker魔咒還持續著，本季Anthony Tucker上場的6場比賽中，只贏了一場，而Talyor Braun上場的6場比賽中，卻贏了三場，導致老師想上課卻沒課上的窘境，之後也會針對此部分做更深入的分析。總體而言，鋼鐵人的OR還在持續往上，沒有下降的趨勢。</p>

<p>防守方面，DR為99.8分，聯盟第五，場均對手2分球命中率51.26%，聯盟最高，場均對手3分球命中率29.86%，聯盟第二高，說明在防守有很大的進步空間，從圖表中可以看出DR最高是在面對夢想家時，讓夢想家以兩分球命中率52%、三分球命中率39.3%，帶走比賽的勝利，不過我認為這跟剛開季的還在磨合有關，現在也逐漸進入比賽狀況，以近況來說，DR日漸下降，防守在強度與戰術下確實有提升，比開季更穩定，搭配持續上升的OR，可以說是往對的方向走，其他球隊要小心了。</p>

<h2 id="六桃園領航猿進攻遇亂流能否安全降落">（六）桃園領航猿，「進攻遇亂流，能否安全降落？」</h2>

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<p>桃園領航猿本季教練團由籃球博士鄭志龍與MVP情人顏行書搭配，進攻強調快狠準，OR為92.73分，聯盟倒數第一，開季前我其實認為領航猿是本土陣容最完整的一支球隊，畢竟大部分都是從SBL璞園建築拉上來的，很多都有中華隊的資歷，也從自由市場補進黃泓瀚與陳昱瑞等人，但從開季至今，似乎沒有達到預期的表現，雖然有些微傷兵困擾，但效率不至於到這麼低，這跟領航猿本土一哥施晉堯的表現脫不了關係，場均投籃命中率從上季42.76%降到33.93%，得分從14.17 分降到10.78分，助攻也從3.63 次降到1.89次，各方面的數據下降，與上季相比的確有些意外，而本季表現突出的69翔，盧峻翔，也因為傷勢只出賽三場，未來是否能健康出賽貢獻進攻端分數，對於領航猿非常關鍵，領航猿另個隱憂是洋將得分佔比約48％，聯盟第二高，僅次於攻城獅，年後本土球員勢必得跳出來分擔進攻，否則有很大的機會下季見。</p>

<p>防守方面，DR為96.61分，聯盟第三，場均對手兩分球命中率51.21%，聯盟第二高，場均對手三分球命中率28.25%，聯盟第二低，本季六隊禁區高度最矮的應該就是領航猿了，為了配合進攻體系，洋將都是選擇200cm上下，活動性佳的球員，所以禁區就比較難守，這部分只能靠包夾與防守輪轉找出效率最高的防守方式，另外在三分外圍防守方面，做得不錯，但我們可以思考是不是外圍防守太積極，使得持球者有機會切入禁區得分？畢竟領航猿不像攻城獅有個辛巴鎮守禁區，不過總體來說，防守表現還行，進攻的問題比較大。</p>

<p>來看看各隊最近的狀況怎麼樣。</p>

<h2 id="g14--g30進攻防守效率圖效率圖">G14 -G30進攻防守效率圖效率圖</h2>

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<p>Fun Facts:</p>

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    <p>攻城師NR為6.91分，聯盟第一，且OR與DR皆高於平均值</p>
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    <p>夢想家的矛與盾，選擇了盾，DR為86.84分，聯盟第一，all in 防守了，還是其實進攻有下，但沒回來？</p>
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    <p>勇士防守近況不佳，DR為105.78分，聯盟墊底</p>
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    <p>鋼鐵人在OR與DR都接近聯盟平均，難怪最近很少看到杜力唱歌，他教球去了</p>
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    <p>國王OR為103.02分，聯盟第一，狂了西提（Crown the City）</p>
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    <p>領航猿的OR為92.61分，聯盟第五，龍哥有領航猿球衣嗎？還是安哥復出了拉</p>
  </li>
</ul>]]></content><author><name></name></author><category term="Basketball" /><summary type="html"><![CDATA[本篇將針對年前戰績後三名，富邦勇士、高雄鋼鐵人與桃園領航猿做分析，以各隊的進攻效率、防守效率與我看比賽的印象為依據，分享我的看法。]]></summary></entry><entry><title type="html">PLG 2021–2022 球季年前 (G01 — G30)各隊分析(上)</title><link href="/posts/PLG-2021-2022-1" rel="alternate" type="text/html" title="PLG 2021–2022 球季年前 (G01 — G30)各隊分析(上)" /><published>2022-01-30T04:31:40+00:00</published><updated>2022-01-30T04:31:40+00:00</updated><id>/posts/PLG-2021-2022-1</id><content type="html" xml:base="/posts/PLG-2021-2022-1"><![CDATA[<blockquote>
  <p>A special thanks to the assistant coach of the New Taipei Kings who appreciated my work! (Check the message below)</p>
</blockquote>

<p>本篇將針對年前戰績前三名，福爾摩沙台新夢想家、新北國王與新竹街口攻城獅做分析，以各隊的進攻效率、防守效率與我看比賽的印象為依據，分享我的看法。</p>

<p><strong>名詞解釋</strong></p>

<blockquote>
  <p>Offense Rating：每100次球權，我方得的分數，簡稱OR</p>

  <p>Defense Rating: 每100次球權，敵方得的分數，簡稱DR</p>

  <p>Net Rating: 每100次球權，我方的淨分(OR 扣除 DR)，簡稱NR</p>
</blockquote>

<h2 id="進攻防守效率圖">進攻防守效率圖</h2>

<p>X軸為進攻效率，Y軸為防守效率，以OR與DR作為判斷依據。</p>

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<p><strong>Fun Facts:</strong></p>

<p>1.攻城獅防守一吉棒棒一吉棒，DR: 91.38分，全聯盟第ㄧ</p>

<p>2.國王與勇士的進攻，真的神仙打架啊，國王OR: 102.28分，勇士OR: 100.76分，但會不會是當天防守都…，才神仙打架？</p>

<p>3.勇士最大的問題不是投不進，是守不住，DR: 100.50分，快跟OR一樣了</p>

<p>4.領航員跟勇士相反，OR: 92.73分，全聯盟倒數第一，年前30場比賽中，全場得分只有60+的球隊，領航員2次</p>

<p>5.夢想家OR: 93.06分，排名第5，年前30場比賽中，全場得分只有60+的球隊，夢想家1次，第三節得5分那次</p>

<p><strong>各隊每場OR、DR折線圖</strong></p>

<p>X 軸為對手，依據時間由早到晚（左到右），Y軸為效率數值</p>

<h2 id="一福爾摩沙台新夢想家誰要carry整隊的進攻">（一）福爾摩沙台新夢想家，「誰要<strong>carry</strong>整隊的進攻」</h2>

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<p>夢想家目前打的9場比賽中，除了那場第三節得5分，被國王痛宰20分外，其餘勝負淨分都在7分以內，從數據來看DR: 95.48分，聯盟第二，防守表現非常不錯，看國王那場就知道了，雖然第三節得5分，但三四節都只讓對手拿18分，值得讚賞。</p>

<p>進攻方面 OR: 93.06分，聯盟倒數第二，我認為多少有被防守影響到，夢想家本季依然主打小球戰術，雖然找了年僅25歲 213cm的中鋒，Brandon Gilbeck，但Gilbeck屬於護筐型中鋒，進攻方面以外援標準來判定是不及格的，另外一位外援 203cm的前鋒，Julian Boyd，雖是全面型的選手，但飽受傷病所苦，進攻表現不穩定，在外援沒辦法穩定輸出分數下，本土球員的得分對球隊極為重要，但有看比賽的人應該了解，夢想家的防守可以說是黏到會牽絲的那種，用命在跟你拼的，雖然的確有了良好的效果，但對於體能來說負荷很大，所以我認為體能的流失也直接影響到進攻表現。</p>

<p>再者，雖然今年補進成力煥、吳永盛與簡浩，但走了楊敬敏（本土場均得分排名第一）一個穩定輸出的得分點，目前看來，沒有人補上那個缺口，從圖表上看出夢想家的進攻表現不穩定，有一場沒一場的，雖然目前戰績第一，但只打了9場比賽，沒辦法斷定此排名能延續到賽季末，況且在賽季前期，有很多球隊都是以不完全體出賽，目前夢想家最大的課題是找出能穩定輸出的球員，來carry 球隊的進攻。</p>

<h2 id="二新北國王進攻端不用煩惱防守端又可以更好">（二）新北國王，「進攻端不用煩惱，防守端又可以更好」</h2>

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<p>國王作為本季的新球隊，9場比賽下的OR竟然來到102.28分，聯盟第一，說明了兩件事情</p>

<p>(1) 毛總很會找球員</p>

<p>(2) 教練團執教功力深不可測</p>

<p>尤其在季初4場只有Thomas Welsh一位洋將下，國王拿下了3勝，本土的能力可以說是非常強，國王9場比賽中，球隊得分為870分，洋將得分為190分，佔比僅21.8%，為聯盟最低，這就說明本土球員於進攻端的貢獻，國王本土球員場均有雙位數得分的球員共4位，楊敬敏21.22分、李愷諺15.11分、簡祐哲11.67分和洪楷傑11.5分，另外Q可以預約第5位，時間早晚問題而已，如果把洋將納入，國王場均雙位數得分共有6位，進攻端的確不需要擔心，雖然在面對到領航員與攻城師（圖中第4、5場）的比賽中，OR急速下降，但兩場比賽國王都是本土應戰，這也說明了Thomas Welsh對國王進攻端的重要性，不但自己可以得分，也可以創造機會給隊友。</p>

<p>防守方面，雖說有聯盟場均抄截王李愷諺，以及聯盟場均阻攻前五名，Thomas Welsh與Q作陣，但DR為97.92分，聯盟第四，屬於中下，看圖表撇除第四場（沒洋將面對領航員），都是在面對勇士隊時DR偏高，目前來看，是有往好的方向發展，在最後一場面對聯盟第二OR的勇士時，把對方得分壓到75分，是非常好的，尤其又加入號稱NBA等級的大鎖 ，辣個守死咖哩的男人，DeAndre Liggins，對於國王來說可以說是如虎添翼，進攻端不用煩惱，防守端又可以更好，衝擊冠軍的機會真的非常高。</p>

<h2 id="三新竹街口攻城獅進攻本土誰來幫">（三）新竹街口攻城獅，「進攻本土誰來幫」</h2>

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<p>攻城獅在11場比賽中的OR為96.09分，聯盟第三，本季洋將沿用上季Brandon Dawson之外，加入了Nick Faust與 Sim Bhullar，Bhullar的加入，對於攻城獅進攻端感覺影響巨大，但實際來說還好，加總Bhullar上場時全隊的分數，攻城獅10場比賽得了共936分，其中Bhullar貢獻250分，約26.7%，而Faust上場時的場次，攻城獅7場比賽得了共686分，其中Faust貢獻172分，約25%，最後Dawson，上場時的場次攻城獅5場比賽得了共434分，其中Dawson貢獻80分，約18.43%，以上為洋將個別佔比，如果是以三位洋將總占比計算，11場比賽攻城獅得了共1028分，三位洋將得了共502分，占比約48.8%，為聯盟第一，球隊接近5成的得分都掌握於洋將手中，是進攻端的隱憂，也是目前攻城獅的課題，不過在年前最後一場比賽，先發變陣，擺上雙後衛，田浩與高國豪，整場比賽本土雙位數得分有三位，分別是田浩、高國豪與宋宇軒，獲得不錯的效果。</p>

<p>防守方面，DR為91.38分，聯盟第一，只要聯盟場均阻攻王Bhullar還站在禁區的一天，防守就不需要擔心，唯一能將Bhullar吸出禁區之外的方法，就是中鋒的外線能力，從圖表中DR最高的那場是面對鋼鐵人，王柏智砍下生涯新高25分，三分球4–9，比賽過程中Bhullar不得不出來對，造成禁區鬧空城，使得鋼鐵人有很大的機會突破得分，另外聯盟場均抄截前五名，Dawson與高國豪分別位居第二與第四，對於攻城獅的防守實屬錦上添花，球傳入禁區有很大的機率被Bhullar蓋，沒蓋到也被影響到，在外圍傳導，也要小心抄截。目前攻城獅防守的弱點是轉換快攻的部分，在Bhullar還沒回來時要如何因應，陣地戰因為Bhullar在禁區，所以外圍可以全力撲防，逼迫進攻者切入面對Bhullar，但轉換快攻時沒人在禁區，所以就很考驗個人防守的能力。</p>

<p><strong><em>下篇除了會分析剩餘三支球隊外，還會有G14-G30的效率圖，看看各隊調整的情況。</em></strong></p>

<p>下篇：<a href="https://jhsu12.github.io/posts/PLG-2021-2022-2">PLG 2021–2022 球季年前(G01 — G30)各隊分析(下)</a></p>

<p>後記，感謝新北國王助理教練Carlos的賞識，我會繼續努力的!</p>

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</table>]]></content><author><name></name></author><category term="Basketball" /><category term="PLG" /><summary type="html"><![CDATA[A special thanks to the assistant coach of the New Taipei Kings who appreciated my work! (Check the message below)]]></summary></entry><entry><title type="html">Watching a Game: Offensive Score Sheets | Basketball on Paper Ch2</title><link href="/posts/Watching-a-Game-Offensive-Score-Sheets" rel="alternate" type="text/html" title="Watching a Game: Offensive Score Sheets | Basketball on Paper Ch2" /><published>2022-01-22T04:31:40+00:00</published><updated>2022-01-22T04:31:40+00:00</updated><id>/posts/Watching-a-Game-Offensive-Score-Sheets</id><content type="html" xml:base="/posts/Watching-a-Game-Offensive-Score-Sheets"><![CDATA[<p>This chapter introduces the offensive score sheets and leads to the concept of possessions. After understanding how to calculate possessions we can then evaluate teams by their offensive rating and defensive rating.</p>

<h2 id="offensive-score-sheets">Offensive Score Sheets</h2>

<p>An offensive score sheet records all most everything that happens with the ball, including the passes, the dribbles, the shots, the rebounds, the steals, the fouls, etc. For me it’s like a more detailed play-by-play transcript of the game.</p>

<p>Let’s take <a href="https://youtu.be/E9XdKW-4h0g">1977 NBA finals G4</a> as an example to demonstrate how Dean Oliver records the score sheet.</p>

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      <td style="text-align: center"><em>Rosters of the Bulls and the Jazz</em></td>
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<p>The Jazz got the first possession. Player 3 had the ball and passed to Player 12, who gave it back to Player 3. Then he passed to Player 14 and Player 14 passed to Player 12 who dribbled. The dribble (D) is usually reserved for when the dribbling is used to gain significant ground, not just a dribble to get balance or a dribble that doesn’t go anywhere beyond where the player is standing.</p>

<p>After Player 12 dribbled, he passed the ball to Player 32 who missed a shot from location B. A missing shot symbolized by the horizontal line and the location of the shot( — location). A made shot without an assist is denoted by (+location), with an assist is denoted by (++location).</p>

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<p>Player 3 was there to rebound (R indicating a rebound), getting it then back out to Player 14 and to Player 12 to reset up on the dribble. He then passed the ball to Player 3 who missed the shot from location Y. The point of the Jazz stayed at 0, the whole possession was ended.</p>

<h2 id="the-important-lesson-of-the-score-sheet-possession">The Important Lesson of the Score Sheet: Possession</h2>

<p>In a game, a team alternates possession with its opponent so that, at the end of the game, each team has just about the same number of possessions on which to try to score. For games involving running or trapping teams, that number of possessions will be high — more than one hundred or 110 in the NBA. For walk-it-up teams, that number of possessions will be low — sometimes fewer than eighty.</p>

<p>How to estimate possession with only FGA, OREB, TOV and FTA?</p>

<blockquote>
  <p><strong>Possessions = FGA-OREB+TOV+0.4*FTA</strong></p>
</blockquote>

<p>It is convenient (and almost correct) to think that there are only three ways for a possession to end: a field goal attempt that is not rebounded by the offense (either a make or a defensive rebound of a miss), a turnover, or some free throws.</p>

<p>We can count these events using some basic stats. A field goal attempt that isn’t rebounded by the offense is simply <strong>FGA-OREB.</strong> A turnover is already counted as <strong>TOV</strong>. Free throws that end possessions are not as well-recorded. Some free throws come in pairs, and the first of the pair can’t end the possession. Some free throws complete three-point plays, which means that the possession is already counted by FGA-OREB term. Dean found that about <strong>40 percent of free throw attempts</strong> end possessions.</p>

<p>A more accurate but complicated formula to estimate possession is</p>

<blockquote>
  <p><strong>Possessions =</strong></p>

  <p><strong>FGA-(OREB/ (OREB+DDREB) ) * (FGA-FGM)<em>1.07+TOV+0.4</em>FTA</strong></p>
</blockquote>

<p>where DDREB represents the defensive rebounds by the opponents.</p>

<h2 id="points-per-possession">Points Per Possession</h2>

<p>After understanding how to estimate possession with simple stats, we can now evaluate a team’s offensive and defensive ratings. An Offensive Rating is the scoring points per 100 possessions. A Defensive Rating is the scoring points of the opponent per 100 possessions.</p>

<p>A team offense is referred to as good. it’s because they’re efficient, not because they score a lot of points. Let’s say the average score of Team A is 119.9 points per game. The average possessions of Team A is 116.9 possessions. Compared to the average score of Team B 89.0 points and the average possessions per game 86.4. If we just focus on how many points a team scored, we’ll ignore how much effort (possessions) a team have made to score. Offensive Rating is a more objective way to evaluate a team offense. The Offensive Rating of Team A is 102.5 points, where Team B is 103.2 points. According to Offensive Rating, we can know that Team B is slightly more efficient than Team A.</p>]]></content><author><name></name></author><category term="Basketball" /><summary type="html"><![CDATA[This chapter introduces the offensive score sheets and leads to the concept of possessions. After understanding how to calculate possessions we can then evaluate teams by their offensive rating and defensive rating.]]></summary></entry></feed>